A Kernel-Independent Sum-of-Exponentials Method

TL;DR

This paper introduces a novel sum-of-exponentials (SOE) approximation method for kernel functions, enabling efficient and accurate convolution quadrature for solving integral equations with controllable error bounds.

Contribution

The paper develops a new SOE approximation combining de la Vallée-Poussin sums and model reduction, improving efficiency and accuracy for general kernels in convolution computations.

Findings

The SOE method achieves high accuracy with fewer exponentials.

Numerical results show significant improvements in efficiency and precision.

The method applies to various kernels and integral equations.

Abstract

We propose an accurate algorithm for a novel sum-of-exponentials (SOE) approximation of kernel functions, and develop a fast algorithm for convolution quadrature based on the SOE, which allows an order $N$ calculation for $N$ time steps of approximating a continuous temporal convolution integral. The SOE method is constructed by a combination of the de la Vall\'ee-Poussin sums for a semi-analytical exponential expansion of a general kernel, and a model reduction technique for the minimization of the number of exponentials under given error tolerance. We employ the SOE expansion for the finite part of the splitting convolution kernel such that the convolution integral can be solved as a system of ordinary differential equations due to the exponential kernels. The significant features of our algorithm are that the SOE method is efficient and accurate, and works for general kernels with controllable upperbound of positive exponents. We provide numerical analysis for both the new SOE method and the SOE-based convolution quadrature. Numerical results on different kernels, the convolution integral and integral equations demonstrate attractive performance of both accuracy and efficiency of the proposed method.