We include here selected recent publications of our group relevant to reduced basis approximation and *a posteriori* error estimation. The ***** to the right of the number indicates papers that develop techniques most closely aligned with the methodology implemented in the rbMIT © MIT software package and also Worked Problems.

Note this list has been recently pruned and re-ordered. We have archived the original list in the event that you might wish to refer to it.

73. **T Taddei, JD Penn, M Yano, and AT Patera, Simulation-Based Classification;
a Model-Order-Reduction Approach for Structural Health Monitoring. Archives of Computational Methods in Engineering special volume on Machine Learning and Computational Mechanics (submitted June 2016). ** *full text available*

We present a Model-Order-Reduction approach to Simulation-Based classification, with particular application to Structural Health Monitoring. The approach exploits (*i*) synthetic results obtained by repeated solution of a parametrized mathematical model for different values of the parameters, (*ii*) machine-learning algorithms to generate a classifier that monitors the damage
state of the system, and (*iii*) a Reduced Basis method to reduce the computational
burden associated with the model evaluations. Furthermore, we propose a mathematical formulation which integrates the partial differential equation model within the classification framework and clarifies the influence of model error on classification performance. We illustrate our approach and we demonstrate its effectiveness through the vehicle of a particular physical companion experiment, a harmonically excited microtruss.

72. **T Taddei, An Adaptive Parametrized-Background Data-Weak Approach to Variational Data Assimilation. Mathematical Modeling and Numerical Analysis (submitted March 2016). ** *full text available*

We present an Adaptive Parametrized-Background Data-Weak (APBDW) approach to the variational data
assimilation (state estimation) problem. The approach is based on the Tikhonov regularization of the PBDW formulation [Y Maday, AT Patera, JD Penn, M Yano, Int J Numer Meth Eng, 102(5), 933-965], and exploits the connection between PBDW and kernel methods for regression. An adaptive procedure is presented to handle the experimental noise.
A priori and a posteriori estimates for the *L ^{2}* state-estimation error motivate the approach and guide the adaptive procedure. We present results for two synthetic
model problems to illustrate the elements of the methodology. We also consider an experimental thermal patch configuration to demonstrate the applicability of our approach to real physical systems.

71. **T Taddei, JD Penn, and AT Patera, Experimental A Posteriori Error Estimation by Monte Carlo Sampling
of Observation Functionals. Mathematical Models and Methods in Applied Sciences (submitted July 2015). ** *full text available*

We present and analyze an experimental *L*^{2}-a posteriori error estimation procedure based on Monte Carlo sampling of observation functionals. Our method provides, given a set of *J* possibly noisy local experimental observation functionals and a state estimate *u** for the true field *u*^{true}, confidence intervals for the *L*^{2} error in state and the error in
*L*^{2} outputs. Our approach implicitly takes advantage of variance reduction, through the
proximity of *u** to *u*^{true}, to provide tight confidence intervals even for modest values
of *J*. We present results for a synthetic model problem to illustrate the elements of the
methodology and confirm the numerical properties suggested by the theory. Finally, we
consider an experimental thermal patch configuration to demonstrate the applicability
of our approach to real physical systems.

70.** K Smetana and AT Patera, Optimal local approximation spaces for component-based static condensation procedures. SIAM Journal on Scientific Computing (submitted February 2015; revised April 2016). ** *full text available*

In this paper we introduce local approximation spaces for component-based static condensation (sc) procedures that are optimal in the sense of Kolmogorov. To facilitate simulations for large structures such as aircrafts or ships it is crucial to decrease the number of degrees of freedom on the interfaces, or ``ports'', in order to reduce the size of the statically condensed system. To derive optimal port spaces we consider a (compact) transfer operator that acts on the space of harmonic extensions on a two-component system and maps the traces on the ports that lie on the boundary of these components to the trace of the shared port. Solving the eigenproblem for the composition of the transfer operator and its adjoint yields the optimal space. For a related work in the context of the generalized finite element method we refer to [I. Babuška and R. Lipton, Optimal local approximation spaces for generalized finite element methods with application to multiscale problems, Multiscale Model. Simul., 9 (2011), pp. 373–406].
We introduce a spectral greedy algorithm to generalize the just described procedure to the parameter dependent setting and construct a quasi optimal parameter independent port space.
Moreover, it is shown that given a certain tolerance and an upper bound for the ports in the system, the spectral greedy constructs a port space that yields a sc approximation error on a system of arbitrary configuration which is smaller than this tolerance for all parameters in a rich train set.
We present our approach for isotropic linear elasticity although the idea is rather general and may be readily applied to any linear coercive problem.
Numerical experiments demonstrate the very rapid and exponential convergence both of the eigenvalues and the sc approximation based on spectral modes.

69.** Y Maday, AT Patera, JD Penn, and M Yano, PBDW state estimation: Noisy observations; configuration-adaptive background spaces; physical interpretations. CANUM 2014 - 42e Congrès National d'Analyse Numérique, Carry-le-Rouet, France, in ESAIM: Proceedings and Surveys, 50:144–168, 2015. ** *full text available* | *doi:10.1051/proc/201550008*

We provide extended analyses and interpretations of the parametrized-background data-weak (PBDW) formulation, a real-time and in-situ data assimilation framework for physical systems modeled by parametrized partial differential equations. The new contributions are threefold. First, we conduct an a priori error analysis for imperfect observations: we provide a bound for the expectation of the state error and identify distinct contributions to the noise-induced error. Second, we illustrate the elements of the PBDW formulation for a physical system, a raised-box acoustic resonator, and provide detailed interpretations of the data assimilation results in particular related to model and data contributions. Third, we devise a strategy to update the PBDW data assimilation system for arbitrary configurations based on the unmodeled physics identified through data assimilation of a select few configurations.

68.** M Yano, A minimum-residual mixed reduced basis method: Exact residual certification and simultaneous finite-element reduced-basis refinement. Mathematical Modelling and Numerical Analysis (submitted August 2014). ** *full text available*

We present a reduced basis method for parametrized partial differential equations certified by a dual-norm bound of the residual computed not in the typical finite-element "truth" space but rather in an infinite-dimensional function space. The bound builds on a finite element method and an associated reduced-basis approximation derived from a minimum-residual mixed formulation. The offline stage combines a spatial mesh adaptation for finite element and greedy parameter sampling strategy for reduced basis to yield a reliable online system in an efficient manner; the online stage provides the solution and the associated dual-norm bound of the residual for any parameter value in complexity independent of the finite element resolution. We assess the effectiveness of the approach for a parametrized reaction-diffusion equation and a parametrized advection-diffusion equation with a corner singularity; not only does the residual bound provide reliable certificates for the solutions, the associated mesh adaptivity significantly reduces the offline computational cost for the reduced-basis generation and the greedy parameter sampling ensures quasi-optimal online complexity.

67.** Y Maday, O Mula, AT Patera, and M Yano, The Generalized Empirical Interpolation Method: Stability theory on Hilbert spaces with an application to the Stokes equation. Computer Methods in Applied Mechanics and Engineering (accepted 2015). Article first published online February 2015. ** *full text available* | * doi: 10.1016/j.cma.2015.01.018 *

The Generalized Empirical Interpolation Method (GEIM) is an extension first presented in [Analysis and Numerics of Partial Differential Equations, Vol. 4 of Springer INdAM Series,
Springer Milan, 2013, pp. 221–235] of the classical empirical
interpolation method (see [CR Acad Sci Paris, Série I 339:667–672, 2004; Math Model Numer Anal 41(3):575–605, 2007; Comm Pure Appl Anal 8(1):383–404, 2009]) where the evaluation at interpolating points is replaced by the evaluation
at interpolating continuous linear functionals on a class of Banach spaces. As outlined in [Analysis and Numerics of Partial Differential Equations, Vol. 4 of Springer INdAM Series,
Springer Milan, 2013, pp. 221–235], this allows to relax the
continuity constraint in the target functions and expand both the application domain and the stability of the approach. In this paper, we present a thorough analysis of the concept of stability condition of the generalized interpolant (the Lebesgue constant) by relating it to an inf-sup problem in the case of Hilbert spaces. In the second part of the paper, it will be explained how GEIM can be employed to monitor in real time physical experiments by providing an online accurate approximation of the phenomenon that is computed by combining the acquisition of a minimal number, optimally placed, measurements from the processes with their mathematical models (parameter-dependent PDEs). This idea is illustrated through a parameter dependent Stokes problem in which it is shown that the pressure and velocity fields can efficiently be reconstructed with a relatively low-dimensional interpolation space.

66.** Y Maday, AT Patera, JD Penn, and M Yano, A parametrized-background data-weak approach to variational
data assimilation: Formulation, analysis, and application to
acoustics. International Journal for Numerical Methods in Engineering (accepted June 2014). Article first published online August 2014.** *full text available* | * doi: 10.1002/nme.4747 *

We present a Parametrized-Background Data-Weak (PBDW) formulation of the variational data assimilation (state estimation) problem for systems modeled by partial differential equations. The main contributions are a constrained optimization weak framework informed by the notion of experimentally observable spaces; a priori and a posteriori error estimates for the field and associated linear-functional outputs; Weak Greedy construction of prior (background) spaces associated with an underlying potentially high–dimensional parametric manifold; stability-informed choice of observation functionals and related sensor locations; and finally, output prediction from the optimality saddle in $\mathcal{O}(M^3)$ operations, where *M* is the number of experimental observations. We present results for a synthetic Helmholtz acoustics model problem to illustrate the elements of the methodology and confirm the numerical properties suggested by the theory. To conclude, we consider a physical raised-box acoustic resonator chamber: we integrate the PBDW methodology and a Robotic Observation Platform to achieve real-time in situ state estimation of the time-harmonic pressure field; we demonstrate the considerable improvement in prediction provided by the integration of a best-knowledge model and experimental observations; we extract even from these results with real data the numerical trends indicated by the theoretical convergence and stability analyses.

65.** K Smetana, A new certification framework for the port reduced static condensation reduced basis element method. Computer Methods in Applied Mechanics and Engineering, 283:352–383, 2015.***full text available* | * doi: 10.1016/j.cma.2014.09.020 *

In this paper we introduce a new certification framework for the port-reduced static condensation reduced
basis element (PR-SCRBE) method, which has been developed for the simulation of large component based
applications such as bridges or acoustic waveguides. In an offline computational stage we construct a library of
interoperable parametrized reference components; in the subsequent online stage we instantiate and connect
the components at the interfaces/ports to form a system of components. To compute a "truth" finite element
approximation of the (say) coercive elliptic partial differential equation on the component based system we
use a domain decomposition approach. For an efficient simulation we employ two different types of model
reduction — a reduced basis (RB) approximation within the interior of the component [D Knezevic, DBP Huynh, AT Patera, ESAIM Math Model Numer Anal 47(1):213–251, 2013] and empirical port reduction [J Eftang, AT Patera, Int J Numer Meth Engng 96(5):269–302, 2013] on the ports where the components connect.
We demonstrate the well-posedness of the PR-SCRBE approximation and introduce a new certification
framework. To assess the quality of the port reduction we use conservative fluxes. We adapt the standard
estimators from RB methods to the SCRBE setting to derive an a posteriori error bound for the RB-error
contribution. In order to combine the a posteriori estimators for both error contributions and derive a rigorous
a posteriori error estimator for PR-SCRBE we adapt techniques from multi-scale methods and component
mode synthesis. Finally, we prove that the effectivity of the derived estimator can be bounded.
We provide numerical experiments for heat conduction and linear elasticity to show that the derived a
posteriori error estimator provides an effective bound. Moreover we demonstrate the applicability of the
introduced certification framework by analyzing the computational (online) costs.

64.** M Yano, A reduced basis method with exact-solution certificates for steady symmetric
coercive equations. Computer Methods in Applied Mechanics and Engineering, (accepted January 2015).
***full text available*

We introduce a reduced basis method that computes rigorous upper and lower bounds of the energy associated with the infinite-dimensional weak solution of parametrized symmetric coercive partial differential equations with piecewise polynomial forcing and operators that admit decompositions that are affine in functions of parameters. The construction of the upper bound appeals to the standard primal variational argument; the construction of the lower bound appeals to the complementary variational principle. We identify algebraic conditions for the reduced basis approximation of the dual variable that results in an exact satisfaction of the dual feasibility conditions and hence a rigorous lower bound. The formulation permits an offline-online computational decomposition such that, in the online stage, the approximation and exact certificates can be evaluated in complexity independent of the underlying finite element discretization. We demonstrate the technique in two numerical examples: a one-dimensional reaction-diffusion problem with a parametrized diffusivity constant; a planar linear elasticity problem with a geometry deformation. We confirm in both cases that the method produces guaranteed upper and lower bounds of the energy at any parameter value for any finite element discretization and reduced basis approximation.

63.** M Yano, JD Penn, and AT Patera, A model-data weak formulation
for simultaneous estimation of state and model bias. Comptes Rendus Mathematique, 351(23-24):937–941, 2013.**
*full text available* | *doi: 10.1016/j.crma.2013.10.034*

We introduce a Petrov-Galerkin regularized saddle approximation which incorporates a "model"
(partial differential equation) and "data" (*M* experimental observations) to yield estimates for both
state and model bias. We provide an a priori theory which identifies two distinct contributions to
the reduction in the error in state as a function of the number of observations, *M*: the stability
constant increases with *M*; the model-bias best-fit error decreases with *M*. We present results for
a synthetic Helmholtz problem and an actual acoustics system.

62.** JL Eftang and AT Patera, A port-reduced static condensation reduced basis element
method for large component-synthesized structures:
Approximation and a posteriori error estimation. Advanced Modeling and
Simulation in Engineering Sciences, 1:3, 2013. ** *doi:10.1186/2213-7467-1-3* (open access paper) | *companion video* (see link for the video under "Additional file 1")

We consider a static condensation reduced basis element framework for efficient approximation of
parameter-dependent linear elliptic partial differential equations in large three-dimensional component based
domains. The approach features an offline computational stage in which a library of interoperable
parametrized components is prepared; and an online computational stage in which these component
archetypes may be instantiated and connected through predefined ports to form a global synthesized
system. In addition to reduced basis approximation in the component interiors, we employ port reduction
with empirical port modes to reduce the number of degrees of freedom on the ports and thus the size of
the Schur complement system. The framework is equipped with efficiently computable a posteriori error
estimators that provide asymptotically rigorous bounds on the error in the approximation with respect
to the underlying finite element discretization.

In this paper, we extend our earlier approach for two-dimensional scalar problems to the more demanding
three-dimensional vector-field case. We focus on linear elasticity analysis for large structures
with tens of millions of finite element degrees of freedom. Through our procedure we effectively reduce
the number of degrees of freedom to a few thousand, and we demonstrate through extensive numerical results
for a microtruss structure that our approach provides an accurate, rapid, and a posteriori verifiable
approximation for relevant large-scale engineering problems.

61.** S Vallaghé, DBP Huynh, D Knezevic, TL Nguyen, and AT Patera, Component-based reduced basis for parametrized symmetric eigenproblems. Advanced Modeling and Simulation in Engineering Sciences, 2:7, 2015. ** *doi:10.1186/s40323-015-0021-0* (open access paper)

A component-based approach is introduced for fast and flexible solution of
parameter-dependent symmetric eigenproblems. Considering a generalized
eigenproblem with symmetric stiffness and mass operators, we start by
introducing a "σ-shifted" eigenproblem where the left-hand side operator
corresponds to an equilibrium between the stiffness operator and a weighted mass
operator, with weight-parameter σ > 0. Assuming that σ = λ_{n} > 0, the *n*^{th} real
positive eigenvalue of the original eigenproblem, then the shifted eigenproblem
reduces to the solution of a homogeneous linear problem. In this context, we can
apply the static condensation reduced basis element (SCRBE) method, a domain
synthesis approach with reduced basis (RB) approximation at the intradomain
level to populate a Schur complement at the interdomain level. In the Offine
stage, for a library of archetype subdomains we train RB spaces for a family of
linear problems; these linear problems correspond to various equilibriums between
the stiffness operator and the weighted mass operator. In the Online stage we
assemble instantiated subdomains and perform static condensation to obtain the
"σ-shifted" eigenproblem for the full system. We then perform a direct search to find the values of σ that yield singular systems, corresponding to the eigenvalues of the original eigenproblem. We provide eigenvalue a posteriori error estimators and we present various numerical results to demonstrate the accuracy,
flexibility
and computational efficiency of our approach. We are able to obtain large speed
and memory improvements compared to a classical Finite Element Method
(FEM), making our method very suitable for large models commonly considered
in an engineering context.

Clarification: In Section 6.5, the industrial example, we consider a Petrov-Galerkin PR-SCRBE approximation (and compare to a Galerkin FE
approximation). The Petrov-Galerkin PR-SCRBE approximation, described for example in paper 65 on this webpage, does not include the
bubble functions in the test space, and thereby greatly reduces the online storage requirements (and also operation count).

60.** M Yano and AT Patera, A space-time variational approach to hydrodynamic stability theory. Proceedings of the Royal Society A, 469(2155): Article Number 20130036, 2013. ** *full text* (April 2013) | *doi: 10.1098/rspa.2013.0036*

We present a hydrodynamic stability theory for incompressible viscous fluid flows
based on a space-time variational formulation and associated generalized singular value
decomposition of the (linearized) Navier-Stokes equations. We first introduce a linear
framework applicable to a wide variety of stationary or time-dependent base flows: we
consider arbitrary disturbances in both the initial condition and the dynamics measured
in a "data" space-time norm; the theory provides a rigorous, sharp (realizable), and
efficiently computed bound for the velocity perturbation measured in a "solution" space-time
norm. We next present a generalization of the linear framework in which the
disturbances and perturbation are now measured in respective selected space-time semi-norms;
the *semi-norm* theory permits rigorous and sharp quantification of, for example,
the growth of initial disturbances or functional outputs. We then develop a (Brezzi-Rappaz-Raviart) nonlinear theory which provides, for disturbances which satisfy a certain
(rather stringent) amplitude condition, rigorous finite-amplitude bounds for the velocity
and output perturbations. Finally, we demonstrate the application of our linear and
nonlinear hydrodynamic stability theory to unsteady moderate Reynolds-number flow in
an eddy-promoter channel.

58.** JL Eftang and AT Patera, Port reduction in parametrized component static condensation: Approximation and a posteriori error estimation. International Journal for Numerical Methods in Engineering, 96(5):269–302, 2013.** *full text available* | *doi: 10.1002/nme.4543*

We introduce a port (interface) approximation and a posteriori error bound framework for a general
component-based static condensation method in the context of parameter-dependent linear elliptic partial
differential equations. The key ingredients are (i) efficient empirical port approximation spaces — the
dimensions of these spaces may be chosen small in order to reduce the computational cost associated
with formation and solution of the static condensation system, and (ii) a computationally tractable a
posteriori error bound realized through a non-conforming approximation and associated conditioner —
the error in the global system approximation, or in a scalar output quantity, may be bounded relatively
sharply with respect to the underlying finite element discretization.

Our approximation and a posteriori error bound framework is of particular computational relevance
for the static condensation reduced basis element (SCRBE) method. We provide several numerical
examples within the SCRBE context which serve to demonstrate the convergence rate of our port approximation
procedure as well as the efficacy of our port reduction error bounds.

57.** M Yano, A Space-Time Petrov-Galerkin Certified Reduced Basis Method: Application to the Boussinesq Equations. SIAM Journal on Scientific Computing, 36(1):A232–A266, 2014.** *full text available* | *doi: 10.1137/120903300*

We present a space-time certified reduced basis method for long-time integration of parametrized parabolic equations with quadratic nonlinearity which admit an affine decomposition in parameter but with no restriction on coercivity of the linearized operator. We first consider a finite element discretization based on discontinuous Galerkin time integration and introduce associated Petrov-Galerkin space-time trial- and test-space norms that yield optimal and asymptotically mesh independent stability constants. We then employ an *hp* Petrov-Galerkin (or minimum residual) space-time reduced basis approximation. We provide the Brezzi-Rappaz-Raviart a posteriori error bounds which admit efficient *offline-online* computational procedures for the three key ingredients: the dual norm of the residual, an inf-sup lower bound, and the Sobolev embedding constant. The latter are based respectively on a more round-off resistant residual norm evaluation procedure, a variant of the successive constraint method, and a time-marching implementation of a fixed-point iteration of the embedding constant for the discontinuous Galerkin norm. Finally, we apply the method to a natural convection problem governed by the Boussinesq equations. The result indicates that the space-time formulation enables rapid and certified characterization of moderate-Grashof-number flows exhibiting steady periodic responses. However, the space-time reduced basis convergence is slow, and the Brezzi-Rappaz-Raviart threshold condition is rather restrictive, such that offline effort will be acceptable only for very few parameters.

56.** S Vallaghé and AT Patera, The Static Condensation Reduced Basis Element Method for a Mixed-Mean Conjugate Heat Exchanger Model. SIAM Journal on Scientific Computing, 36(3),
B294--B320, 2014.**
*full text available* | * doi: 10.1137/120887709*

We propose a new approach for the simulation of conjugate heat exchangers. First, we introduce
a dimensionality-reduced mathematical model for conjugate (fluid-solid) heat transfer: in the fluid
channels, we consider a mixed-mean temperature defined on one-dimensional filaments; in the solid we
consider a detailed partial differential equation (PDE) conduction representation. We then propose
a Petrov-Galerkin finite element (FE) numerical approximation which provides suitable stability
and accuracy for our mathematical model. We next apply the static condensation reduced basis
element (scRBE) method: a domain synthesis approach with parametric model order reduction at
the intradomain level to populate a Schur complement at the interdomain level. We first build a
library of "components," each corresponding to a subdomain with a simple fluid channel geometry;
for each component, we prepare Petrov-Galerkin reduced basis bubble approximations (and error
bounds). We then assemble the system equations by static condensation and solve for the temperature
distribution in the full domain. System-level error bounds are derived from matrix perturbation
arguments; we also introduce a new output error bound which is sharper than the original scRBE
estimator. We present numerical results for a two-dimensional automotive radiator model which
demonstrate the flexibility, accuracy, and computational efficiency of our approach.

55.** DBP Huynh, DJ Knezevic, and AT Patera, A Static Condensation Reduced Basis Element Method: Complex
Problems. Computer Methods in Applied Mechanics and Engineering, 259:197-216, 2013.**
*full text available* | *doi: 10.1016/j.cma.2013.02.013*

We extend the static condensation reduced basis element (scRBE) method to treat the
class of parametrized complex Helmholtz partial differential equations. The main ingredients
are (*i*) static condensation at the interdomain level, (*ii*) a conforming eigenfunction
"port" representation at the interface level, (*iii*) the reduced basis (RB) approximation of finite element (FE) bubble functions at the intradomain level, and (*iv*) rigorous system-level
error bounds which reflect RB perturbation of the FE Schur complement. We then incorporate
these ingredients in an Offine-Online computational strategy to achieve rapid and
accurate prediction of parametric systems formed as instantiations of interoperable
parametrized archetype components from a Library. We introduce a number of extensions
with respect to the original scRBE framework: first, primal-dual RB methods for general
non-symmetric (complex) problems; second, stability constant procedures for weakly coercive
problems (at both the interdomain level and intradomain level); third, treatment of
non-port linear-functional outputs (as well as functions of outputs); fourth, consideration of
particular components and outputs relevant to acoustic applications. We consider several
numerical examples in acoustics (in particular focused on mufflers and horns) to demonstrate
that the approach can handle models with many parameters and/or topology variations with
particular reference to waveguide-like applications.

54.** M Yano, AT Patera, and K Urban, A Space-Time hp-Interpolation-Based Certified Reduced Basis Method for Burgers' equation. Mathematical Models and Methods in Applied Sciences, 24(9):1903--1935, 2014.**

*full text available*|

*doi: 10.1142/S0218202514500110*

We present a space-time interpolation-based certified reduced basis method for Burgers' equation over the spatial interval (0,1) and the temporal interval (0,

*T*] parametrized with respect to the Peclet number. We first introduce a Petrov-Galerkin space-time finite element discretization which enjoys a favorable inf-sup constant that decreases slowly with Peclet number and final time

*T*. We then consider an

*hp*interpolation-based space-time reduced basis approximation and associated Brezzi-Rappaz-Raviart a posteriori error bounds. We describe computational

*offline-online*decomposition procedures for the three key ingredients of the error bounds: the dual norm of the residual, a lower bound for the inf-sup constant, and the space-time Sobolev embedding constant. Numerical results demonstrate that our space-time formulation provides improved stability constants compared to classical

*L*

^{2}-error estimates; the error bounds remain sharp over a wide range of Peclet numbers and long integration times

*T*, in marked contrast to the exponentially growing estimate of the classical formulation for high Peclet number cases.

53.** DBP Huynh, A Static Condensation Reduced Basis Element Approximation: Application
to three-dimensional acoustic muffler analysis. International Journal of Computational Methods, 11(3):1343010 (16 pages), 2014.**
*full text available* | * doi: 10.1142/S021987621343010X *

We apply the static condensation reduced basis element (scRBE) method to treat the class of parametrized complex Helmholtz partial differential equations. We construct a set of components of interoperable parametrized reference components in a Library to model a family of target models relevant to acoustic devices. The components in the Library are built upon the scRBE method by using RB formulation, and are compatible to each other by a set of common interfaces, or ports. We apply an Offline-Online computational strategy to achieve rapid and accurate prediction of any parametric systems formed from a set of components in a Library. We demonstrate that the approach can handle large scale models with many parameters and/or topology variations efficiency in several numerical examples. We show that significant computational savings can be obtained by the scRBE method.

52.** JL Eftang and B Stamm, Parameter Multi-Domain " hp" Empirical Interpolation. International Journal for Numerical Methods in Engineering, 90(4):412–428, 2012.**

*full text available*|

*doi: 10.1002/nme.3327*

In this paper, we introduce two parameter multi-domain "

*hp*" techniques for the empirical interpolation method (EIM). In both approaches, we construct a partition of the original parameter domain into parameter subdomains:

*h*-refinement. We apply the standard EIM independently within each subdomain to yield local (in parameter) approximation spaces:

*p*-refinement. Further, for a particularly simple case we introduce a priori convergence theory for the partition procedure. We show through two numerical examples that our approaches provide significant reduction in the EIM approximation space dimension, and thus significantly reduce the computational cost associated with EIM approximations.

51.** K Urban and AT Patera, An Improved Error Bound for Reduced Basis Approximation of Linear Parabolic Problems. Mathematics of Computation, 83(288):1599--1615, 2014 (published online October 2013).**
*full text available* | *doi: 10.1090/S0025-5718-2013-02782-2*

We consider a space-time variational formulation for linear parabolic partial differential equations. We introduce an associated Petrov-Galerkin truth finite element discretization with favorable discrete inf-sup constant *β _{δ}*, the inverse of which enters into error estimates:

*β*is unity for the heat equation;

_{δ}*β*decreases only linearly in time for non-coercive (but asymptotically stable) convection operators. The latter in turn permits effective long-time a posteriori error bounds for reduced basis approximations, in sharp contrast to classical (pessimistic) exponentially growing energy estimates. The paper contains a full analysis and various extensions for the formulation introduced briefly by K Urban and AT Patera [CR Acad Sci Paris Series I, 350(3-4):203–207, 2012] as well as numerical results for a model reaction-convection-diffusion equation.

_{δ} 50.** AT Patera and EM Rønquist, Regression on Parametric Manifolds: Estimation of Spatial Fields,
Functional Outputs, and Parameters from Noisy Data. CR Acad Sci Paris, Series I, 350(9-10):543-547, 2012.**
*full text *(revised May 2012) | *doi:10.1016/j.crma.2012.05.002*

In this Note we extend the Empirical Interpolation Method (EIM) to a regression context which accommodates
noisy (experimental) data on an underlying parametric manifold. The EIM basis functions are computed Offline
from the noise-free manifold; the EIM coefficients for any function on the manifold are computed Online from
experimental observations through a least-squares formulation. Noise-induced errors in the EIM coefficients and
in linear-functional outputs are assessed through standard confidence intervals and without knowledge of the
parameter value or the noise level. We also propose an associated procedure for parameter estimation from noisy
data.

49.** JL Eftang, DBP Huynh, DJ Knezevic, EM Rønquist, and AT Patera, Adaptive Port Reduction in Static
Condensation. Proceedings of 7th Vienna Conference on Mathematical Modelling (MATHMOD 2012), eds. I Troch and F Breitenecker, Mathematical Modelling, 7(1):695--699, 2012.**
*full text * | doi: 10.3182/20120215-3-AT-3016.00123

We introduce a framework for adaptive reduction of the degrees of freedom associated with ports in static condensation (SC). We apply this framework to the SC reduced basis (RB) method and thus combine parametric model order reduction for the interior of a component with model order reduction on the ports in order to rapidly construct an approximate Schur complement linear system of reduced size. The port reduction framework invokes quasi-rigorous
a posteriori error bounds for adaptation and allows a combination of *empirical* functions (snapshot-based) and eigenfunctions for the representation of the solution on the ports.

48.** JL Eftang, MA Grepl, AT Patera, and EM Rønquist, Approximation of Parametric Derivatives by the
Empirical Interpolation Method. Foundations of Computational Mathematics, 13(5):763–787, 2013.**
*full text available* |* doi: 10.1007/s10208-012-9125-9*

We introduce a general a priori convergence result for the approximation of parametric derivatives of parametrized functions. We consider the best approximations to parametric derivatives in a sequence of approximation spaces generated by a general approximation scheme, and we show that these approximations are convergent provided that the best approximation to the function itself is convergent. We also provide estimates for the convergence rates. We present numerical results with spaces generated by a particular approximation scheme — the Empirical Interpolation Method — to confirm the validity of the general theory.

47.** N Jung, AT Patera, B Haasdonk, and B Lohmann, Model Order Reduction and Error Estimation with an
Application to the Parameter-Dependent Eddy Current Equation. Mathematical and Computer Modelling of Dynamical Systems 17(6):561–582, 2011.**
*doi: 10.1080/13873954.2011.582120*

In product development, engineers simulate the underlying partial differential equation many times with commercial tools for different geometries. Since the available computation time is limited, we look for reduced models with an error estimator that guarantees the accuracy of the reduced model. Using commercial tools the theoretical methods proposed by G Rozza, DBP Huynh and AT Patera [Arch Comput Methods Eng 15:229–275] lead to technical difficulties. We present how to overcome these challenges and validate the error estimator by applying it to a simple model of a solenoid actuator that is a part of a valve.

46.** K Urban and AT Patera, A New Error Bound for Reduced Basis Approximation of
Parabolic Partial Differential Equations. CR Acad Sci Paris Series I, 350(3-4):203–207, 2012.**
*full text available* | *doi:10.1016/j.crma.2012.01.026*

We consider a space-time variational formulation for linear parabolic partial
differential equations. We introduce an associated Petrov-Galerkin truth
finite element discretization with favorable discrete inf-sup constant \beta_\delta: \beta_\delta
is bounded from below by unity for the heat equation; \beta_\delta grows only linearly
in time for non-coercive (asymptotically stable) convection operators. The
latter in turn permits eective long-time a posteriori error bounds for reduced
basis approximations, in sharp contrast to classical exponentially growing
energy estimates.

45.** DBP Huynh, DJ Knezevic, and AT Patera, A Static Condensation Reduced Basis Element Method: Approximation and A Posteriori Error Estimation. Mathematical Modelling and Numerical Analysis, 47(1): 213–251, 2013.**
*full text available* (15 May 2012) | *doi:10.1051/m2an/2012022*

We propose a new reduced basis element-cum-component mode synthesis approach for
parametrized elliptic coercive partial differential equations. In the Offline stage we construct a
Library of interoperable parametrized reference *components* relevant to some family of problems;
in the Online stage we instantiate and connect reference components (at ports) to rapidly form and
query parametric *systems*. The method is based on static condensation at the interdomain level, a
conforming eigenfunction "port" representation at the interface level, and finally reduced basis (RB)
approximation of finite element (FE) bubble functions at the intradomain level. We show under
suitable hypotheses that the RB Schur complement is close to the FE Schur complement: we can
thus demonstrate the stability of the discrete equations; furthermore, we can develop inexpensive
and rigorous (system-level) a posteriori error bounds. We present numerical results for model many-parameter heat transfer and elasticity problems with particular emphasis on the Online stage; we
discuss flexibility, accuracy, computational performance, and also the effectivity of the a posteriori
error bounds.

44.** A Quarteroni, G Rozza, and A Manzoni, Certified Reduced Basis Approximation for Parametrized
Partial Differential Equations and Applications. Journal of Mathematics in Industry 2011, 1:3 (3 June 2011). **
*http://www.mathematicsinindustry.com/content/1/1/3*

Reduction strategies, such as model order reduction (MOR) or reduced basis (RB) methods, in scientific
computing may become crucial in applications of increasing complexity. In this paper we review the reduced basis
method (built upon a high-fidelity "truth" finite element approximation) for a rapid and reliable approximation of
parametrized partial differential equations, and comment on their potential impact on applications of industrial
interest. The essential ingredients of RB methodology are: a Galerkin projection onto a low-dimensional space
of basis functions properly selected, an affine parametric dependence enabling to perform a competitive Offline-Online splitting in the computational procedure, and a rigorous a posteriori error estimation used for both the
basis selection and the certification of the solution. The combination of these three factors yields substantial
computational savings which are at the basis of an efficient model order reduction, ideally suited for real-time
simulation and many-query contexts (e.g. optimization, control or parameter identification). After a brief excursus
on the methodology, we focus on linear elliptic and parabolic problems, discussing some extensions to more general
classes of problems and several perspectives of the ongoing research. We present some results from applications
dealing with heat and mass transfer, conduction-convection phenomena, and thermal treatments.

43.** KC Hoang, BC Khoo, GR Liu, NC Nguyen, and AT Patera, Rapid identification of material properties of the interface tissue in dental implant systems using reduced basis method. Journal of Inverse Problems in Science and Engineering (submitted March 2011).**
*full text available*

This paper proposes a rapid inverse analysis approach based on the reduced basis method and
the Levenberg-Marquardt-Fletcher algorithm to identify the "unknown" material properties:
Young's modulus and stiffness-proportional Rayleigh damping coefficient of the interfacial
tissue between a dental implant and the surrounding bones. In the forward problem, a finite
element approximation for a three-dimensional dental implant-bone model is first built. A reduced basis approximation is then established by using a Proper Orthogonal Decomposition
(POD)-Greedy algorithm and the Galerkin projection to enable extremely fast and reliable
computation of displacement responses for a range of material properties. In the inverse analysis, the reduced basis approximation for the dental implant-bone model are incorporated in
the Levenberg-Marquardt-Fletcher algorithm to enable rapid identification of the unknown
material properties. Numerical results are presented to demonstrate the efficiency and robustness of the proposed method.

42.** DBP Huynh, DJ Knezevic, and AT Patera, Certified Reduced Basis Model Characterization: a Frequentistic Uncertainty Framework. Computer Methods in Applied Mechanics and Engineering. Computer Methods in Applied Mechanics and Engineering 201–204:13–24, 2012.**
*full text available* |*doi:10.1016/j.cma.2011.09.011*

We introduce a frequentistic validation framework for assessment — acceptance or rejection — of the consistency
of a proposed parametrized partial differential equation model with respect to (noisy) experimental
data from a physical system. Our method builds upon the Hotelling *T*^{ 2} statistical hypothesis test for bias first introduced by Balci & Sargent [Am J
Math Manage Sci 4(3-4):375–406, 1984] and subsequently extended by McFarland & Mahadevan [CMAME 197(29–32):2467–2479, 2008] .
Our approach introduces two new elements: a spectral representation of the misfit which reduces the dimensionality
and variance of the underlying multivariate Gaussian but without introduction of the usual
regression assumptions; a certified (verified) reduced basis approximation — reduced order model — which
greatly accelerates computational performance but without any loss of rigor relative to the full (finite element)
discretization. We illustrate our approach with examples from heat transfer and acoustics, both
based on synthetic data. We demonstrate that we can efficiently identify possibility regions that characterize
parameter uncertainty; furthermore, in the case that the possibility region is empty, we can deduce the
presence of "unmodeled physics" such as cracks or heterogeneities.

**ERRATA** Corollary 3.1 is stated in a misleading way. We should state instead in
the following fashion:

If $s^{PS} = s_{h}$ then ${\cal H}$ of (30) is true; Proposition 2.1 then
applies. If ${\cal H}$ of (30) is false, then (31) (of Corollary 3.1)
demonstrates that $s^{PS} \neq s_{h}$.

41.** JL Eftang, DBP Huynh, DJ Knezevic, and AT Patera, A Two-Step Certified Reduced Basis Method. Journal of Scientific Computing 51(1):28–58, 2012.**
*full text available* | *doi: 10.1007/s10915-011-9494-2*

In this paper we introduce a two-step Certified Reduced Basis (RB) method. In the first step we construct from an expensive finite element “truth” discretization of dimension \calN an intermediate RB model of dimension $N \ll {\cal N}$. In the second step we construct from this intermediate RB model a *derived* RB (DRB) model of dimension *M* ≤ *N*. The construction of the DRB model is effected at cost ${\cal O} (N)$ and in particular at cost independent of ${\cal N}$; subsequent evaluation of the DRB model may then be effected at cost ${\cal O} (M)$. The DRB model comprises both the DRB output *and* a rigorous a posteriori error bound for the error in the DRB
output with respect to the *truth* discretization.

The new approach is of particular interest in two contexts: *focus calculations *and* hp-RB approximations*. In the former the new approach serves to reduce online cost, $M \ll N$: the DRB model is restricted to a slice or subregion of a larger parameter domain associated with the intermediate RB model. In the latter the new approach enlarges the class of problems amenable to *hp*-RB treatment by a significant reduction in offline (precomputation) cost: in the development of the *hp* parameter domain partition and associated “local” (now derived) RB models the finite element truth is replaced by the intermediate RB model. We present numerical results to illustrate the new approach.

40.** DBP Huynh, DJ Knezevic, and AT Patera, A Laplace Transform Certified Reduced Basis Method;
Application to the Heat Equation and Wave Equation. CR Acad Sci Paris Series I, 349(7-8):401–405, 2011.**
*full text available* | *doi:10.1016/j.crma.2011.02.003*

We present a certified reduced basis (RB) method for the heat equation and wave equation. The critical ingredients are certified RB approximation of the Laplace transform; the inverse Laplace transform to develop the time-domain RB output approximation and rigorous error bound; a (Butterworth) filter in time to effect the necessary "modal"
truncation; RB eigenfunction decomposition and contour integration for Offine-Online decomposition. We present numerical results to demonstrate the accuracy and efficiency of the approach.

39.** DJ Knezevic and JW Peterson, A High-Performance Parallel Implementation of the Certified Reduced Basis Method. Computer Methods in Applied Mechanics and Engineering 200(13-16):1455–1466, 2011.**
*full text available* (revised December 2010) | *doi: 10.1016/j.cma.2010.12.026*

The Certified Reduced Basis method (herein RB method) is a popular approach for model reduction of parametrized partial differential equations. In this paper we introduce new techniques that are required
to efficiently implement the Offine "Construction stage" of the RB method on high-performance parallel supercomputers. This enables us to generate certified RB models for large-scale three-dimensional problems that can be evaluated on standard workstations and other "thin" computing resources with speedup of many orders of magnitude compared to the corresponding full order model. We use our implementation to perform detailed numerical studies for two computationally expensive model problems: a natural convection fluid flow problem and a "many parameter" heat transfer problem. In the heat transfer problem, we exploit the
computational efficiency of the RB method to perform a detailed study of "snapshot" selection in the Greedy algorithm, and we also examine statistics of the output sensitivity derivatives to obtain a "global" view of the relative importance of the parameters.

38.** * JL Eftang, DJ Knezevic, and AT Patera, An hp Certified Reduced Basis Method for Parametrized Parabolic Partial Differential Equations. Mathematical and Computer Modelling of Dynamical Systems, 17(4):395–422, 2011. **

*full text available*|

*doi:10.1080/13873954.2011.547670*

In this paper we introduce an

*hp*certified reduced basis method for parabolic partial differential equations. We invoke a POD (in time) / Greedy (in parameter) sampling procedure first in the initial partition of the parameter domain (

*h*-refinement) and subsequently in the construction of reduced basis approximation spaces restricted to each parameter subdomain (

*p*-refinement). We show that proper balance between additional POD modes and additional parameter values in the initial subdivision process guarantees convergence of the approach. We present numerical results for two model problems: linear convection-diffusion and quadratically nonlinear Boussinesq natural convection. The new procedure is significantly faster (respectively, more costly) in the reduced basis Online (respectively, Offline) stage.

37.** * DJ Knezevic, Reduced Basis Approximation and A Posteriori Error Estimates for a Multiscale Liquid Crystal Model. Mathematical and Computer Modelling of Dynamical Systems 17(4):443–461, 2011.**
*full text available* | *doi: 10.1080/13873954.2011.547676*

We present a reduced basis framework and associated *a posteriori* error estimates for the multiscale Stokes Fokker-Planck system that governs the flow of a dilute suspension of rod-like molecules immersed in a Newtonian solvent, relevant in liquid crystals modeling. The Fokker-Planck equation dictates the microscale behavior and must be solved at every quadrature point of the macroscale finite element mesh — this is a natural example of a many-query problem for which the certified reduced basis method is well suited. We focus on a Poiseuille flow problem to simplify the presentation of ideas, but we note that the methods developed in this paper generalize directly to more complicated problems. Numerical results demonstrate that our reduced basis approach leads to significant computational savings and also that our error estimator performs well for moderate parameter values.

36.** * DBP Huynh, DJ Knezevic, JW Peterson, and AT Patera, High-Fidelity Real-Time Simulation on Deployed Platforms. Computers and Fluids, 43(1):74–81, 2011.
** *full text available* | *doi:10.1016/j.compfluid.2010.07.007*

We present a certified reduced basis method for high-fidelity real-time solution of parametrized partial differential
equations on deployed platforms. Applications include *in situ* parameter estimation, adaptive design and control,
interactive synthesis and visualization, and individuated product specification.
We emphasize a new hierarchical architecture particularly well suited to the reduced basis
computational paradigm: the expensive Offline stage is conducted pre-deployment on a parallel supercomputer
(in our examples, the TeraGrid machine Ranger); the inexpensive Online stage is conducted "in the field" on
ubiquitous thin/inexpensive platforms such as laptops, tablets, smartphones
(in our examples, the Nexus One Android-based phone), or embedded chips. We illustrate our approach with three examples: a two-dimensional Helmholtz acoustics "horn" problem; a three-dimensional transient heat conduction "Swiss Cheese" problem; and a three-dimensional unsteady incompressible Navier-Stokes low-Reynolds-number "eddy-promoter" problem.

35.** * JL Eftang, MA Grepl, and AT Patera, A Posteriori Error Bounds for the Empirical Interpolation Method. CR Acad Sci Paris Series I, 348(9–10): 575–579, 2010. **
*full text* (revised March 2010) | *doi:10.1016/j.crma.2010.03.004*

We present rigorous *a posteriori* error bounds for the Empirical Interpolation Method (EIM). The essential ingredients are (*i*) analytical upper bounds for the parametric derivatives of the function to be approximated, (*ii*) the EIM “Lebesgue constant,” and (*iii*) information concerning the EIM approximation error at a finite set of points in parameter space. The bound is computed “offline” and is valid over the entire parameter domain; it is thus readily employed in (say) the “online” reduced basis context. We present numerical results that confirm the validity of our approach.

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34.** * JL Eftang, AT Patera, and EM Rønquist, An “ hp” Certified Reduced Basis Method for Parametrized Elliptic Partial Differential Equations. SIAM Journal on Scientific Computing, 32(6):3170–3200 (2010).**

*full text*(revised July 2010) |

*doi: 10.1137/090780122*

We present a new “

*hp*” parameter multi-domain certified reduced basis method for rapid and reliable online evaluation of functional outputs associated with parametrized elliptic partial differential equations. We propose, and provide theoretical justification for, a new procedure for adaptive partition (“

*h*”-refinement) of the parameter domain into smaller parameter subdomains: we pursue a hierarchical splitting of the parameter (sub)domains based on proximity to judiciously chosen parameter anchor points within each subdomain. Subsequently, we construct individual standard RB approximation spaces (“

*p*”-refinement) over each subdomain. Greedy parameter sampling procedures and

*a posteriori*error estimation play important roles in both the “

*h*”-type and “

*p*”-type stages of the new algorithm. We present illustrative numerical results for a convection-diffusion problem: the new “

*hp*”-approach is considerably faster (respectively, more costly) than the standard “

*p*”-type reduced basis method in the online (respectively, offline) stage.

33.** Y Maday, NC Nguyen, AT Patera, and SH Pau, A General Multipurpose Interpolation Procedure: The Magic Points. Communications on Pure and Applied Analysis (CPAA), 8(1):383–404, 2009.
** *doi: 10.3934/cpaa.2009.8.383*

Lagrangian interpolation is a classical way to approximate general functions by finite sums of well chosen, pre-defined, linearly independent interpolating functions; it is much simpler to implement than determining the best fits with respect to some Banach (or even Hilbert) norms. In addition, only partial knowledge is required (here values on some set of points). The problem of defining the best sample of points is nevertheless rather complex and is in general open. In this paper we propose a way to derive such sets of points. We do not claim that the points resulting from the construction explained here are optimal in any sense. Nevertheless, the resulting interpolation method is proven to work under certain hypothesis, the process is very general and simple to implement, and compared to situations where the best behavior is known, it is relatively competitive.

32.** S Boyaval, C Le Bris, T Lelièvre, Y Maday, NC Nguyen, and AT Patera, Reduced Basis Techniques for Stochastic Problems. Archives of Computational Methods in Engineering 17(4):435–454, 2010. ** *full text available* (revised March 2010) | *doi: 10.1007/s11831-010-9056-z*

In this review paper, we look at recent applications of a now classical general reduction technique, the Reduced Basis (RB) approach initiated in [Prud'homme, et al., JFE, 124(1):70–80, 2002], to the specific context of differential equations with random coefficients. First we give an overview of the RB approach. Next we look at two specific applications. The first is the application of the RB approach for the discretization of a simple second-order elliptic equation with a random boundary condition [S Boyaval, et al., CMAME, 198(41-44):3187–3206, 2009]; the second is the application of the RB approach to reduce the variance in the Monte-Carlo simulation of a stochastic differential equation [S Boyaval and T. Lelièvre, A variance reduction method for parametrized stochastic differential equations using the reduced basis paradigm. In P Zhang, editor, Communication in Mathematical Sciences, volume Special Issue “Mathematical Issues on Complex Fluids”, accepted for publication 2009]. Finally, we conclude with some general comments and a discussion of possible tracks for further research.

31.** A Buffa, Y Maday, AT Patera, C Prud'homme, and G Turinici, A Priori Convergence of the Greedy Algorithm for the Parametrized Reduced Basis. Mathematical Modelling and Numerical Analysis 46(3):595–603, 2012. ** *full text available* | *doi: 10.1051/m2an/2011056*

The convergence and efficiency of the reduced basis method used for the approximation of the solutions to a class of problems written as a parametrized PDE depends heavily on the choice of the elements that constitute the "reduced basis."
The purpose of this paper is to analyze the a priori convergence for one of the approaches used for the selection of these elements, the greedy algorithm. Under natural hypothesis on the set of all solutions to the problem obtained when the parameter varies, we prove that three greedy algorithms converge; the last algorithm, based on the use of an a posteriori estimator, is the approach actually employed in the calculations.

30.** DJ Knezevic, NC Nguyen, and AT Patera, Reduced Basis Approximation and A Posteriori Error Estimation for the Parametrized Unsteady Boussinesq Equations. Mathematical Models and Methods in Applied Sciences 21(7):1415–1442, 2011. ** *full text available* | *doi: 10.1142/S0218202511005441*

In this paper we present reduced basis approximations and associated rigorous *a posteriori* error bounds for the parametrized unsteady Boussinesq equations. The essential ingredients are Galerkin projection onto a low-dimensional space associated with a smooth parametric manifold — to provide dimension reduction; an efficient POD-Greedy sampling method for identification of optimal and numerically stable approximations — to yield rapid convergence; accurate (Online) calculation of the solution-dependent stability factor by the Successive Constraint Method — to quantify the growth of perturbations/residuals in time; rigorous *a posteriori* bounds for the errors in the reduced basis approximation and associated outputs — to provide certainty in our predictions; and an Offline-Online computational decomposition strategy for our reduced basis approximation and associated error bound — to minimize marginal cost and hence achieve high performance in the real-time and many-query contexts. The method is applied to a transient natural convection problem in a two-dimensional “complex” enclosure — a square with a small rectangle cut-out — parametrized by Grashof number and orientation with respect to gravity. Numerical results indicate that the reduced basis approximation converges rapidly and that furthermore the (inexpensive) rigorous *a posteriori* error bounds remain practicable for parameter domains and final times of physical interest.

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29.** JL Eftang, AT Patera, and EM Rønquist, An “ hp” Certified Reduced Basis Method for Parametrized Parabolic Partial Differential Equations. JS Hesthaven and EM Rønquist (eds.), Spectral and High Order Methods for Partial Differential Equations, Lecture Notes in Computational Science and Engineering, 76:179–187, 2011. **

*full text*(revised January 2010) |

*doi: 10.1007/978-3-642-15337-2_15*

We extend previous work on a parameter multi-element “

*hp*” certified reduced basis method for elliptic equations to the case of parabolic equations. A POD (in time) / Greedy (in parameter) sampling procedure is invoked both in the partitioning of the parameter domain (“

*h*”-refinement) and in the construction of individual reduced basis approximation spaces for each parameter subdomain (“

*p*”-refinement). The critical new issue is proper balance between additional POD modes and additional parameter values in the initial subdivision process. We present numerical results to compare the computational cost of the new approach to the standard (“

*p*”-type) reduced basis method.

28.** JL Eftang and EM Rønquist, Evaluation of Flux Integral Outputs for the Reduced Basis Method. Mathematical Models and Methods in Applied Sciences, 20(3): 351–374, 2010. ** *full text* (revised October 2009) | *doi: 10.1142/S021820251000426X*

In this paper, we consider the evaluation of flux integral outputs from reduced basis solutions to second-order PDE’s. In order to evaluate such outputs, a lifting function *v** must be chosen. In the standard finite element context, this choice is not relevant, whereas in the reduced basis context, as we show, it greatly affects the output error. We propose two “good” choices for *v** and illustrate their effect on the output error by examining a numerical example. We also make clear the role of *v** in a more general primal-dual reduced basis approximation framework.

27.** * NC Nguyen, G Rozza, DBP Huynh, and AT Patera, Reduced Basis Approximation and A Posteriori Error Estimation for Parametrized Parabolic PDEs; Application to Real-Time Bayesian Parameter Estimation. In Large-Scale
Inverse Problems and Quantification of Uncertainty, L Biegler, G Biros, O Ghattas, M Heinkenschloss, D Keyes, B Mallick, Y
Marzouk, L Tenorio, B van Bloemen Waanders, and K. Willcox (eds), John Wiley & Sons, Ltd, UK. (to appear 2010).** full text available

In this paper we first review our most recent RB techniques — approximation and *a posteriori* error estimation — for linear parabolic equations, with particular emphasis on combined POD (in time) - Greedy (in parameter) sampling procedures as first proposed by Haasdonk and Ohlberger [M2AN 42(2):277–302, 2008]; as a numerical example, we consider transient thermal analysis of a delamination crack in a two-material (Fiber-Reinforced-Polymer and concrete) system. We also consider the application of these RB approximations and error bounds — within the context of our particular crack delamination example — to Bayesian parameter estimation: we provide very rapid *and certified* RB parameter estimators that permit effectively real-time (reliable) response.

We observe numerically that the Bayesian upper bound for our parameter estimate
increases linearly with the RB error bound when the RB error bound is
sufficiently smaller than the experimental error, but exponentially when the RB
error bound is significantly larger than the experimental error. Therefore, the
RB error bound must be chosen smaller than the experimental error to obtain
sharp Bayesian bounds.

26.** * DBP Huynh, DJ Knezevic, Y Chen, JS Hesthaven, and AT Patera, A Natural-Norm Successive Constraint Method for Inf-Sup Lower Bounds. Computer Methods in Applied Mechanics and Engineering, 199:1963–1975, 2010.**
*full text* (revised February 2010) | *doi: 10.1016/j.cma.2010.02.011*

We present a new approach for the construction of lower bounds for the inf-sup stability constants required in *a posteriori* error analysis of reduced basis approximations to affinely parametrized partial differential equations. We combine the "linearized" inf-sup statement of the natural-norm approach with the approximation procedure of the Successive Constraint Method (SCM): the former (natural-norm) provides an economical parameter expansion and local concavity in parameter — a small(er) optimization problem which enjoys intrinsic lower bound properties; the latter (SCM) provides a systematic optimization framework — a Linear Program (LP) relaxation which readily incorporates continuity and stability constraints. The natural-norm SCM requires a parameter domain decomposition: we propose a greedy algorithm for selection of the SCM control points as well as adaptive construction of the optimal subdomains. The efficacy of the natural-norm SCM is illustrated through numerical results for two types of non-coercive problems: the Helmholtz equation (for acoustics, elasticity, and electromagnetics), and the convection-diffusion equation.

25.** * DJ Knezevic and AT Patera, A Certified Reduced Basis Method for the Fokker-Planck Equation of Dilute Polymeric Fluids: FENE Dumbbells in Extensional Flow. SIAM Journal on Scientific Computing, 32(2):793–817, 2010.**
*full text* (revised December 2009) | *doi:10.1137/090759239*

In this paper we present a reduced basis method for the parametrized Fokker-Planck equation associated with evolution of Finitely Extensible Nonlinear Elastic (FENE) dumbbells in a Newtonian solvent for a (prescribed) extensional macroscale flow. There are two new important ingredients: a projection-based POD-Greedy sampling procedure (proposed by Haasdonk and Ohlberger [M2AN 42(2):277–302, 2008]) for the stable identification of optimal reduced basis spaces; and a finite-time *a posteriori* bound for the error in the reduced basis prediction of the two outputs of interest — the optical anisotropy and the first normal stress difference. We present numerical results for stress-conformation hysteresis as a function of Weissenberg number and final time that demonstrate the rapid convergence of the reduced basis approximation and the effectiveness of the *a posteriori* error bounds.

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24.** * S Boyaval, C Le Bris, Y Maday, NC Nguyen, and AT Patera, A Reduced Basis Approach for Variational Problems with Stochastic Parameters: Application to Heat Conduction with Variable Robin Coefficient. Computer Methods in Applied Mechanics and Engineering, 198(41-44):3187–3206, 2009.
** *doi: 10.1016/j.cma.2009.05.019*

In this work, a Reduced Basis (RB) approach is used
to solve a large number of Boundary Value Problems (BVPs)
parametrized by a stochastic input — expressed as a Karhunen-Loève expansion — in order to compute outputs that are smooth functionals of the random solution fields.
The RB method proposed here for variational problems parametrized by stochastic coefficients
bears many similarities to the RB approach developed previously for deterministic systems.
However, the stochastic framework requires the development of new *a posteriori* estimates
for "statistical" outputs — such as the first two moments of integrals of the random solution fields; these error bounds, in turn, permit
efficient sampling of the input stochastic parameters
and fast reliable computation of the outputs in particular in the many-query context.

23.** * NC Nguyen, G Rozza, & AT Patera, Reduced Basis Approximation and A Posteriori Error Estimation for the Time-Dependent Viscous Burgers' Equation. Calcolo, 46(3):157–185 2009.**

*doi: 10.1007/s10092-009-0005-x*

In this paper we present rigorous

*a posteriori*

*L*

^{2}error bounds for reduced basis approximations of the unsteady viscous Burgers' equation in one space dimension. The

*a posteriori*error estimator, derived from standard analysis of the error-residual equation, comprises two key ingredients — both of which admit efficient Offline-Online treatment: the first is a sum over timesteps of the square of the dual norm of the residual; the second is an accurate upper bound (computed by the Successive Constraint Method) for the exponential-in-time stability factor. These error bounds serve both Offline for construction of the reduced basis space by a new POD-Greedy procedure and Online for verification of fidelity. The

*a posteriori*error bounds are practicable for final times (measured in convective units)

*T*≈

*O*(1) and Reynolds numbers ν

^{ −1}≫ 1; we present numerical results for a (stationary) steepening front for

*T*= 2 and 1 ≤ ν

^{ −1}≤ 200.

Note: Extension of the results of this paper to Navier-Stokes can be found in Paper 30.

22.** * G Rozza, NC Nguyen, DBP Huynh, and AT Patera, Real-Time Reliable Simulation of Heat Transfer Phenomea. Proceedings of HT2009, 2009 ASME Summer Heat Transfer Conference, July 19–23, 2009, San Francisco, California, paper number HT2009-88212.
** *full text available*

This paper includes, in addition to a brief summary
of the certified reduced basis methodology, a description of the rbMIT software package, a "worked problem" framework for educational applications of the rbMIT software, and finally several illustrative "worked problems" in heat transfer.

21.** S Sen, Reduced-Basis Approximation and A Posteriori Error Estimation for Many-Parameter Heat Conduction Problems.
Numerical Heat Transfer, Part B: Fundamentals 54(5):369–389, 2008.**

*doi:10.1080/10407790802424204*

Reduced-basis (RB) methods enable repeated and rapid evaluation of parametrized partial differential equation (PDE)-constrained input-output relationships required in the context of parameter estimation, design, optimization, and control. These methods have been successfully applied to problems with few parameters [

*O*(3)]. Here we introduce efficient sampling algorithms that enable the efficient exploration of many parameters. We apply the RB methods to an illustrative heat conduction problem with

*P*= 25 parameters, obtaining accurate and certified results in real time with significant computational savings relative to standard finite-element techniques.

20.** * G Rozza, DBP Huynh, and AT Patera, Reduced Basis Approximation and A Posteriori Error Estimation for Affinely Parametrized Elliptic Coercive Partial Differential Equations — Application to Transport and Continuum Mechanics.
Archives of Computational Methods in Engineering 15(3):229–275, 2008.**

*doi: 10.1007/s11831-008-9019-9*

This paper provides a comprehensive review of (Lagrange) reduced basis approximation and

*a posteriori*error estimation for linear coercive elliptic PDEs. We also provide a summary of the "state of the art" of RB methods for PDEs more generally as well as an extensive bibliography.

We discuss the formulation of geometric and coefficient parametric variations consistent with affine parameter dependence; we describe primal-dual RB Galerkin approximation and associated energy and output optimality results; we compare several sampling strategies — in particular, POD and greedy methods — for generation of effective RB spaces; we provide theoretical and computational evidence of RB convergence in the single and many parameter cases; we summarize our RB

*a posteriori*(output) error bounds and associated theoretical effectivity results; we present the Successive Constraint Method for construction of the coercivity constant lower bounds required by our

*a posteriori*error estimators; we describe the Offline-Online computational procedures and associated operation counts for RB output approximation and

*a posteriori*error estimation; we provide computational results — convergence, effectivity, and performance — for several problems in transport (conduction, advection-diffusion), inviscid flow (added mass), and elasticity (Stress Intensity Factors, effective properties).

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19.** * S Boyaval, Reduced-Basis Approach for Homogenization Beyond the Periodic Setting. SIAM Multiscale Modeling and Simulation 7(1):466–494, 2008.** *doi: 10.1137/070688791*

This paper considers the development of the reduced basis method for the computation of averaged coefficients for the homogenization of elliptic partial differential equations. In the (non-periodic) homogenization context, as in many multiscale frameworks, we must effect a large number of similar calculations on a microscale cell for property and geometric parameter values induced by macroscale "distribution" functions. This multi-query computational task can be very efficiently effected by the offline-online reduced basis approach; the reduced basis method further provides rigorous *a posteriori* error bounds for the effective properties — which can in turn be integrated into error estimators for the ultimate macroscale predictions.

*** DBP Huynh, G Rozza, S Sen, and AT Patera, A Successive Constraint Linear Optimization Method for Lower Bounds of Parametric Coercivity and Inf-Sup Stability Constants. CR Acad Sci Paris Series I 345:473–478, 2007.**

*doi: 10.1016/j.crma.2007.09.019*

This paper presents an approach to the construction of lower bounds for the coercivity and inf-sup stability constants required in

*a posteriori*error analysis of reduced basis approximations to affinely parametrized partial differential equations. The method, based on an Offline-Online strategy relevant in the reduced basis many-query and real-time context, reduces the Online calculation to a small Linear Program: the objective is a parametric expansion of the underlying Rayleigh quotient; the constraints reflect stability information at optimally selected parameter points. The method is simple and general to implement, the Offline stage is based on standard eigenproblems that can be efficiently treated by the Lanczos method, and the Online Linear Program is typically of modest size.

Numerical results are presented for an (coercive) elasticity problem and an (non-coercive) acoustics Helmholtz problem.

**ERRATA**Note we should consider the symmetric part in the formation of the $\hat{a}^{q}$ leading up to Equation(6).

17.** AT Patera and E. Rønquist, Reduced Basis Approximations and A Posteriori Error Estimation for a Boltzmann Model. Computer Methods in Applied Mechanics and Engineering, 196(29-30): 2925–2942, 2007.***doi:10.1016/j.cma.2007.02.008*

In this paper we consider RB approximation and *a posteriori* error estimation for a simple one-dimensional (in space) linearized BGK Boltzmann model for flow in a channel at transitional Knudsen numbers: the inputs are the Knudsen number and accommodation coefficient; the output is the channel flowrate. This first-order hyperbolic ("non-local") problem is first consistently reformulated through a "Strange Upwind Petrov Galerkin" procedure as a second-order non-symmetric coercive elliptic problem (in one space dimension, no variational crimes are committed): this stable representation — with suitable mappings to treat the infinite molecular-velocity domain — then serves to define both the (spectral element) truth approximation and the subsequent RB treatment. The RB output approximation converges rapidly; and the RB *a posteriori* error estimators (based on an *ad hoc* coercivity lower bound) is respectable — though for smaller RB errors the effectivities can occasionally be large.

16.** * DBP Huynh and AT Patera, Reduced Basis Approximation and A Posteriori Error Estimation for Stress Intensity Factors. International Journal for Numerical Methods in Engineering, 72(10):1219–1259, 2007.*** * *doi: 10.1002/nme.2090 *

This paper introduces a new formulation for quadratic outputs: we first transform a (coercive or non-coercive) elliptic equation with *quadratic outputs* to a non-coercive elliptic equation (of twice the size) with *linear compliant* outputs; we then consider RB approximation and *a posteriori* error estimation for this expanded system. The output error bounds for the expanded linear system are both simpler and considerably sharper than the output bounds for the original quadratic representation.

We derive and apply this new "expanded" approach to the calculation of the Energy Release Rate and hence Stress Intensity Factor (SIF) associated with Mode-I linear elastic cracks (ultimately relevant to fracture). Many-query and real-time reliable prediction of the SIF can be crucial: for fatigue-induced evolution of a nascent crack; for "in-the-field" assessment of (brittle) failure. We present RB results for three examples: a heterogeneous two-layer notch; a center-crack test specimen; and a hole-crack configuration. (The truth FE model is based on singularity-enriched spaces.)

15.** É Cancès, C Le Bris, Y Maday, NC Nguyen, AT Patera, and GSH Pau, Feasibility and Competitiveness of a Reduced Basis Approach for Rapid Electronic Structure Calculations in Quantum Chemistry. Centre de Recherches Mathématiques : CRM Proceedings and Lecture Notes, 41: 15–57, 2007.**

In this paper we consider the application of the RB approach to several model problems in quantum mechanics: a Hartree Fock approximation of diatomic hydrogen; and a simple Density-Functional Theory Kohn-Sham approximation to an idealized one-dimensional crystalline solid. The paper focuses on several of the new difficulties associated with RB treatment of electronic structure (eigenproblem-like) calculations: efficient evaluation of higher-order and non-polynomial nonlinearities (see Paper 13); economical representation of fields defined over high-dimensional product spaces; and (related) efficient incorporation of mutual orthogonality constraints.

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14.** * G Rozza and K Veroy, On the Stability of the Reduced Basis Method for Stokes Equations in Parametrized Domains. Computer Methods in Applied Mechanics and Engineering, 196(7): 1244–1260 2007.***doi:10.1016/j.cma.2006.09.005*

This paper focuses on proper treatment of the pressure in the incompressible Stokes system. It is very simple to construct parametric Stokes problems and simple RB approximations for which the discrete "Brezzi" inf-sup parameter is identically zero and the pressure error order unity. One possible solution is a divergence-free RB velocity space. This paper considers a second possible solution: the introduction of an enriched velocity space to ensure a good discrete Brezzi inf-sup parameter and associated good pressure approximation. Several options of varying degrees of rigor are proposed: in each case the velocity space is expanded to include "Brezzi" supremizers; the various options are distinguished by the sample selection and subsequent post-processing.

13.** * MA Grepl, Y Maday, NC Nguyen, and AT Patera, Efficient reduced-basis treatment of nonaffine and nonlinear partial differential equations. Mathematical Modelling and Numerical Analysis (M2AN), 41(3):575–605, 2007.***doi: 10.1051/m2an:2007031*

This paper expands and elaborates upon Paper 6, and in particular extends our non-affine methodology to efficiently treat transcendentally nonlinear (in fact, semi-linear) elliptic and parabolic equations. RB methods based on standard Galerkin approaches are not efficient for greater-than-quadratic nonlinearities; the methods are particularly inefficient — the online complexity scales with the dimension of the finite element "truth" approximation space,
— for non-polynomial nonlinearities. Our "empirical interpolation" approach recovers -independence in the online stage for general nonlinearities. Note: independent investigators (C Prud'homme et al.) indicate that the numerical results of Section 5 correspond to the test problem of equations (52) and (53) but for f(v) = 100 sin(2\pi x_1) sin(2\pi x_2) (and not f(v) = 100 sin(2\pi x_1) cos(2\pi x_2) as stated in the paper).

See also Nguyen and Grepl theses. Also G Rozza, Reduced basis method for Stokes equations in domains with non-affine parametric dependence, *Comp Vis Science*, 12(1):23–35, 2009, Rozza thesis, and AE Løvgren,
Reduced basis modeling of hierarchical flow systems, PhD Thesis, Norwegian University of Science and Technology, Trondheim, Norway, 2005.

12.** * S Sen, K Veroy, DBP Huynh, S Deparis, NC Nguyen, and AT Patera, "Natural Norm" A Posteriori Error Estimators for Reduced Basis Approximations. Journal of Computational Physics, 217 (1): 37–62, 2006.***doi: j.jcp.2006.02.012*

In this paper we propose a new "natural norm" formulation for our reduced basis error estimation framework that (*a*) improves our inf-sup lower bound construction (offline) and evaluation (online) — a critical ingredient of our *a posteriori* error estimators, and (*b*) significantly sharpens our output error bounds, in particular (through deflation) for parameter
values corresponding to nearly singular solution behavior.

We apply the method to two illustrative problems: a coercive Laplacian heat conduction problem — which becomes singular as the heat transfer coefficient tends to zero; and a noncoercive
Helmholtz acoustics problem — which becomes singular as we approach resonance.

11. **IB Oliveira and AT Patera, Reduced-Basis Techniques for Rapid Reliable Optimization of Systems Described by Affinely Parametrized Coercive Elliptic Partial Differential Equations. Optimization and Engineering, 8(1):43–65, 2007.*** doi: 10.1007/s11081-007-9002-6*

This paper considers the application of our RB approximations and associated error bounds to constrained optimization problems. Particular focus is placed on (*i*) efficient calculation of higher-order (gradient, Hessian) information, and (*ii*) rigorous incorporation of our error bounds — which ensures that our RB-generated optimizer is in fact feasible with respect to the truth finite element approximation. (For a single optimization offline effort will dominate — and our method is not efficient; however for many optimizations or for real-time applications, online effort will dominate — and our method becomes advantageous.)

The paper considers the example of optimization of a thermal fin with respect to heat transfer coefficient and geometry.

10.** MA Grepl, NC Nguyen, K Veroy, AT Patera, and GR Liu, Certified Rapid Solution of Partial Differential Equations for Real-Time Parameter Estimation and Optimization. In Real-time PDE-Constrained Optimization, (L. Biegler, O. Ghattas, M. Heinkenschloss, D. Keyes, and B. van Bloemen Waanders, eds.) SIAM Computational Science and Engineering Book Series, 2007, pp 197–215.***full text available *

This paper reviews our methodology for noncoercive (Helmholtz) elliptic equations, the Navier-Stokes equations, and parabolic equations, from the perspective of real-time and reliable computation.

The paper includes an example of real-time robust parameter estimation in the context of nondestructive evaluation of a crack in an elastic medium. We can rapidly deduce — from the response of the medium to tuned harmonic forcing — the "possibility" set of all crack lengths consistent with experimental measurements. No regularization or unverifiable prior assumptions are required: well-posedness is manifested in the shrinking of our "possibility" set as the experimental error tends to zero. The key computational enabler is the RB output prediction and associated *a posteriori* error estimator.

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9.** * NC Nguyen, K Veroy, and AT Patera, Certified Real-Time Solution of Parametrized Partial Differential Equations. In Handbook of Materials Modeling, (S. Yip, editor), Springer (2005), pp. 1523–1558.***full text available *

This mini-review paper summarizes in pedagogical fashion our most recent approaches for non-coercive equations (Helmholtz) and quadratically nonlinear equations (the incompressible Navier-Stokes equations). Note that for the Navier-Stokes equations we now explicitly include the pressure through introduction of appropriate "Brezzi" supremizers in the velocity space, as discussed in more detail in Paper 14 (see also G. Rozza, Real-time reduced basis techniques in arterial bypass geometries, *Proceedings of the Third M.I.T. Conference on Computational Fluid and Solid Mechanics*, June 14–17, 2005, (K.J. Bathe, ed.) Elsevier, pp. 1284–1287).

Examples include a crack in a linear elastic medium subject to harmonic forcing; and Boussinesq natural convection at Pr = 0.7 (for which the velocity, pressure, *and* temperature are all now coupled) in a cavity.

This paper also reviews the adaptive sample construction procedure introduced in Paper 4.

8.** * MA Grepl and AT Patera, A Posteriori Error Bounds for Reduced-Basis Approximations of Parametrized Parabolic Partial Differential Equations. Mathematical Modelling and Numerical Analysis (M2AN), 39(1):157–181, 2005.***doi:10.1051/m2an:2005006*

This paper extends our methodology to time-dependent problems, in particular linear parabolic equations of the heat-equation type: we introduce RB approximations (primal and dual) for time-dependent outputs; rigorous *a posteriori* error bounds in energy and output norms; efficient offline-online computational decompositions; and adaptive greedy sampling procedures in [time, parameter] space.

See also Grepl thesis and DV Rovas, et al., Reduced Basis Output bounds methods for parabilic problems, *IMA J Appl Math*, 26(3):423–445, 2006.

7.** * K Veroy and AT Patera, Certified Real-Time Solution of the Parametrized Steady Incompressible Navier-Stokes Equations: Rigorous Reduced-Basis A Posteriori Error Bounds. Int. J. Numer. Meth. Fluids 47:773–788, 2005.***doi:10.1002/fld.867 *

This paper extends our Brezzi-Rappaz-Raviart formulation for the Burgers equation of Paper 5 to the full incompressible Navier-Stokes equations (over divergence-free spaces); the paper also improves the requisite inf-sup lower bound construction. The method is applied to moderate-Grashof-number natural convection at zero Prandtl number in a cavity of aspect ratio four.

6.** * M Barrault, Y Maday, NC Nguyen, and AT Patera, An 'Empirical Interpolation' Method: Application to Efficient Reduced-Basis Discretization of Partial Differential Equations. CR Acad Sci Paris Series I 339:667–672, 2004.***doi:10.1016/j.crma.2004.08.006*

This paper relaxes the condition of affine parameter dependence: we propose a method for the treatment of linear elliptic coercive PDEs with general parametric dependence. The technique replaces nonaffine coefficient functions with an appropriately constructed collateral RB expansion and associated interpolaton (or "collocation") procedure that then permits an (effectively) affine offline-online computational decomposition. See Paper 13 for elaboration and extension.

The associated *a posteriori* error indicators are not upper bounds, and hence there is some loss in rigor relative to classical Galerkin treatment.

5.** * K Veroy, C Prud'homme, and AT Patera, Reduced-Basis Approximation of the Viscous Burgers Equation: Rigorous A Posteriori Error Bounds. CR Acad Sci Paris Series I 337:619–624, 2003.***doi:10.1016/j.crma.2003.09.023
*

This paper develops rigorous error bounds and associated offline-online computational procedures for the quadratically nonlinear Burgers equation. The essential new ingredient is a fully computational interpretation and associated Offline-Online development of the classical Brezzi-Rappaz-Raviart theory for analysis of variational approximations of nonlinear problems.

We remark that, for general nonlinear problems, our error bounds can only be conditional; however, the necessary "proximity" criterion — related to the size of the residual (and hence ) — can be rigorously verified online, and hence there is no loss in computational efficiency or rigor.

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4. **K Veroy, C Prud'homme, DV Rovas, and AT Patera, A Posteriori Error Bounds for Reduced-Basis Approximation of Parametrized Noncoercive and Nonlinear Elliptic Partial Differential Equations. AIAA Paper 2003-3847, Proceedings of the 16th AIAA Computational Fluid Dynamics Conference, 2003.** *full text available*

This paper develops the first rigorous error bounds for noncoercive problems (the Helmholtz problem — see also Y Maday, et al., A Blackbox Reduced-Basis Output Bound Method for Noncoercive Linear Problems, in *Nonlinear Partial Differential Equations and Their Applications*, Collége de France Seminar Volume XIV (eds D Cioranescu and J-L Lions),
Elsevier Science B.V., pp. 533–569, 2002, and Rovas thesis) and for nonlinear problems (a monotonic cubic nonlinearity); additionally, we discuss a first quasi-rigorous treatment for the Burgers equation.

The proposals in this paper for the inf-sup lower bound (required for our error estimators for noncoercive equations), the treatment of greater-than-quadratic nonlinearities, and the error bounds for the Burgers equation, have all been subsequently improved: see Papers 12 and 18, Paper 13, and Papers 5 and 7 and 9, respectively.

This paper also introduces an adaptive sampling procedure which exploits our error bounds to construct very efficient RB samples and hence spaces — essentially a POD-like selection approach *but in which only the retained snapshots* (amongst a very large number of candidates) are actually computed on the FEM truth approximation space. This sampling approach is further articulated in Paper 7, Paper 8, and Paper 9.

**ERRATA** Note the Helmholtz deflation procedure described in this paper is not correct.

3.** * Y Maday, AT Patera, and G Turinici, Global A Priori Convergence Theory for Reduced-Basis Approximations of Single-Parameter Symmetric Coercive Elliptic Partial Differential Equations. CR Acad Sci Paris Series I 335:289–294, 2002**.

*doi:10.1016/S1631-073X(02)02466-4*

This paper develops the

*a priori*theory for RB convergence in a particularly simple single-parameter context. The emphasis is two-fold: demonstration of

*uniform/global*convergence of RB approximations — quantification of the "smooth, low-dimensional manifold'' argument; and motivation of an

*a priori*parameter sample distribution — logarithmic — that performs quite well in practice.

2.** * C Prud'homme, DV Rovas, K Veroy, L Machiels, Y Maday, AT Patera, and G Turinici, Reliable Real-Time Solution of Parametrized Partial Differential Equations: Reduced-Basis Output Bound Methods. J Fluids Engineering, 124:70–80, 2002.** *doi:10.1115/1.1448332*

This paper summarizes the basic ingredients — global RB approximation spaces, *a posteriori* error estimators, and affine parameter dependence and associated offline-online computational procedures — for the case of linear coercive elliptic PDEs (see also L Machiels, et.al., A blackbox reduced-basis output bound method for shape optimization, *Proceeding of the 12th International Conference on Domain Decomposition Methods*, DDM.org, 1999). Particular emphasis is on error estimation for both symmetric and non-symmetric problems and both compliant and non-compliant outputs; for non-symmetric or non-compliant problems, adjoint techniques are proposed. Examples include a thermal fin and an elastic truss system.

Note that our software is built on the rigorous "Method I'' error estimators; the "Method II'' error estimators are only asymptotic, and hence not currently implemented in our software.

1. **L Machiels, Y Maday, IB Oliveira, AT Patera, and DV Rovas, Output Bounds for Reduced-Basis Approximations of Symmetric Positive Definite Eigenvalue Problems. CR Acad Sci Paris Series I 331:153–158, 2000. ** *doi:10.1016/S0764-4442(00)00270-6*

This paper introduces the basic ingredients — global RB approximation spaces, *a posteriori* error estimators, and affine parameter dependence and associated offline-online computational procedures — in the context of a symmetric positive-definite (Laplacian) eigenproblem. Note that for the (nonlinear) eigenvalue example of this paper, the error bounds proposed are only asymptotic (as ), and hence are not currently implemented in our software.

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