Learning complex nonlinear physical systems using wavelet neural operators  

作　　者：Yanan Guo Xiaoqun Cao Hongze Leng Junqiang Song 郭亚楠;曹小群;冷洪泽;宋君强

机构地区：[1]College of Meteorology and Oceanography,National University of Defense Technology,Changsha 410073,China [2]College of Computer,National University of Defense Technology,Changsha 410073,China [3]Naval Aviation University,Huludao 125001,China

出　　处：《Chinese Physics B》2025年第3期461-472,共12页中国物理B(英文版)

基　　金：Project supported by the National Natural Science Foundation of China(Grant Nos.42005003 and 41475094)。

摘　　要：Nonlinear science is a fundamental area of physics research that investigates complex dynamical systems which are often characterized by high sensitivity and nonlinear behaviors.Numerical simulations play a pivotal role in nonlinear science,serving as a critical tool for revealing the underlying principles governing these systems.In addition,they play a crucial role in accelerating progress across various fields,such as climate modeling,weather forecasting,and fluid dynamics.However,their high computational cost limits their application in high-precision or long-duration simulations.In this study,we propose a novel data-driven approach for simulating complex physical systems,particularly turbulent phenomena.Specifically,we develop an efficient surrogate model based on the wavelet neural operator(WNO).Experimental results demonstrate that the enhanced WNO model can accurately simulate small-scale turbulent flows while using lower computational costs.In simulations of complex physical fields,the improved WNO model outperforms established deep learning models,such as U-Net,Res Net,and the Fourier neural operator(FNO),in terms of accuracy.Notably,the improved WNO model exhibits exceptional generalization capabilities,maintaining stable performance across a wide range of initial conditions and high-resolution scenarios without retraining.This study highlights the significant potential of the enhanced WNO model for simulating complex physical systems,providing strong evidence to support the development of more efficient,scalable,and high-precision simulation techniques.

关 键 词：nonlinear science TURBULENCE deep learning wavelet neural operator 

分 类 号：O17[理学—数学]