Coarse-proxy reduced basis methods for integral equations.

In this paper, we introduce a new reduced basis methodology for accelerating the computation of large parameterized systems of high-fidelity integral equations. Core to our methodology is the use of coarse-proxy models (i.e., lower resolution variants of the underlying high-fidelity equations) to identify important samples in the parameter space from which a high quality reduced basis is then constructed. Unlike the more traditional POD or greedy methods for reduced basis construction, our methodology has the benefit of being both easy to implement and embarrassingly parallel. We apply our methodology to the under-served area of integral equations, where the density of the underlying integral operators has traditionally made reduced basis methods difficult to apply. To handle this difficulty, we introduce an operator interpolation technique, based on random sub-sampling, that is aimed specifically at integral operators. To demonstrate the effectiveness of our techniques, we present two numerical case studies, based on the radiative transport equation and a boundary integral formation of the Laplace equation respectively, where our methodology provides a significant improvement in performance over the underlying high-fidelity models for a wide range of error tolerances. Moreover, we demonstrate that for these problems, as the coarse-proxy selection threshold is made more aggressive, the approximation error of our method decreases at an approximately linear rate. Finally, we provide a public repository of our source code with easy instructions for reproducing all results in this paper. • Presents novel model reduction method for many-query integral equation problems. • Uses column-pivoted QR and coarse proxy models to efficiently extract training samples for a reduced basis. • Sparse sampling of integral operators allows for efficient reconstruction of Galerkin projected operators. • Benchmarks on radiative transport and Laplace equation show a 30× speedup with only a 4% sacrifice to accuracy. [ABSTRACT FROM AUTHOR]