Efficient Scaling and Moving Techniques for Spectral Methods in Unbounded Domains

TL;DR

This paper introduces adaptive scaling and moving techniques for spectral methods in unbounded domains, improving convergence and efficiency when solving PDEs with solutions that expand or translate.

Contribution

The paper proposes novel frequency-based scaling and exterior-error-based moving techniques to enhance spectral methods for unbounded domain PDEs, enabling adaptive point clustering.

Findings

Achieves spectral convergence in various models including Fermi-Dirac distributions.

Outperforms existing scaling approaches in linear parabolic problems.

Effectively tracks solution blowup in cell proliferation models.

Abstract

When using Laguerre and Hermite spectral methods to numerically solve PDEs in unbounded domains, the number of collocation points assigned inside the region of interest is often insufficient, particularly when the region is expanded or translated to safely capture the unknown solution. Simply increasing the number of collocation points cannot ensure a fast convergence to spectral accuracy. In this paper, we propose a scaling technique and a moving technique to adaptively cluster enough collocation points in a region of interest in order to achieve a fast spectral convergence. Our scaling algorithm employs an indicator in the frequency domain that is used to determine when scaling is needed and informs the tuning of a scaling factor to redistribute collocation points to adapt to the diffusive behavior of the solution. Our moving technique adopts an exterior-error indicator and moves the collocation points to capture the translation. Both frequency and exterior-error indicators are defined using only the numerical solutions. We apply our methods to a number of different models, including diffusive and moving Fermi-Dirac distributions and nonlinear Dirac solitary waves, and demonstrate recovery of spectral convergence for time-dependent simulations. Performance comparison in solving a linear parabolic problem shows that our frequency scaling algorithm outperforms the existing scaling approaches. We also show our frequency scaling technique is able to track the blowup of average cell sizes in a model for cell proliferation.