A Novel Paradigm in Solving Multiscale Problems

TL;DR

This paper introduces a new decoupling paradigm using Spectral PINNs to efficiently simulate multiscale phenomena across various complex systems, significantly reducing computational costs while maintaining accuracy.

Contribution

It proposes a novel decoupling approach for multiscale problems and develops a Spectral PINN to accurately model small-scale dynamics independently.

Findings

Demonstrates effectiveness on Navier-Stokes and Kuramoto-Sivashinsky equations

Shows versatility in handling complex geometries and noisy data

Reduces computational demands for large-scale multiscale simulations

Abstract

Multiscale phenomena manifest across various scientific domains, presenting a ubiquitous challenge in accurately and effectively simulating multiscale dynamics in complex systems. In this paper, a novel decoupling solving paradigm is proposed through modelling large-scale dynamics independently and treating small-scale dynamics as a slaved system. A Spectral Physics-informed Neural Network (PINN) is developed to characterize the small-scale system in an efficient and accurate way, addressing the challenges posed by the representation of multiscale dynamics in neural networks. The effectiveness of the method is demonstrated through extensive numerical experiments, including one-dimensional Kuramot-Sivashinsky equation, two- and three-dimensional Navier-Stokes equations, showcasing its versatility in addressing problems of fluid dynamics. Furthermore, we also delve into the application of the proposed approach to more complex problems, including non-uniform meshes, complex geometries, large-scale data with noise, and high-dimensional small-scale dynamics. The discussions about these scenarios contribute to a comprehensive understanding of the method's capabilities and limitations. By enabling the acquisition of large-scale data with minimal computational demands, coupled with the efficient and accurate characterization of small-scale dynamics via Spectral PINN, our approach offers a valuable and promising approach for researchers seeking to tackle multiscale phenomena effectively.