Efficient and accurate nonlinear model reduction via first-order empirical interpolation

TL;DR

This paper introduces a first-order empirical interpolation method (FOEIM) that leverages derivative information to efficiently and accurately reduce nonlinear PDE models, improving computational performance over traditional methods.

Contribution

The paper develops a novel FOEIM that uses derivative data to enhance nonlinear model reduction, offering two algorithms for interpolation point and basis construction.

Findings

FOEIM improves computational efficiency for nonlinear PDEs.

Numerical results show FOEIM outperforms EIM and Galerkin methods.

FOEIM maintains accuracy while reducing complexity.

Abstract

We present a model reduction approach that extends the original empirical interpolation method to enable accurate and efficient reduced basis approximation of parametrized nonlinear partial differential equations (PDEs). In the presence of nonlinearity, the Galerkin reduced basis approximation remains computationally expensive due to the high complexity of evaluating the nonlinear terms, which depends on the dimension of the truth approximation. The empirical interpolation method (EIM) was proposed as a nonlinear model reduction technique to render the complexity of evaluating the nonlinear terms independent of the dimension of the truth approximation. We introduce a first-order empirical interpolation method (FOEIM) that makes use of the partial derivative information to construct an inexpensive and stable interpolation of the nonlinear terms. We propose two different FOEIM algorithms to generate interpolation points and basis functions. We apply the FOEIM to nonlinear elliptic PDEs and compare it to the Galerkin reduced basis approximation and the EIM. Numerical results are presented to demonstrate the performance of the three reduced basis approaches.