2024-09-08 Sunday Sign in CN

Activities
A hybrid iterative method based on MIONet for PDEs: Theory and numerical examples
Home - Activities
Reporter:
Pengzhan Jin, Research Assistant Professor, National Engineering Laboratory for Big Data Analysis and Applications, Peking University
Inviter:
Yifa Tang, Professor
Subject:
A hybrid iterative method based on MIONet for PDEs: Theory and numerical examples
Time and place:
9:00-10:00 July 9(Tuesday), N702
Abstract:

We propose a hybrid iterative method based on MIONet for PDEs, which combines the traditional numerical iterative solver and the recent powerful machine learning method of neural operator, and further systematically analyze its theoretical properties, including the convergence condition, the spectral behavior, as well as the convergence rate, in terms of the errors of the discretization and the model inference. We show the theoretical results for the frequently-used smoothers, i.e. Richardson (damped Jacobi) and Gauss-Seidel. We give an upper bound of the convergence rate of the hybrid method w.r.t. the model correction period, which indicates a minimum point to make the hybrid iteration converge fastest. Several numerical examples including the hybrid Richardson (Gauss-Seidel) iteration for the 1-d (2-d) Poisson equation are presented to verify our theoretical results, and also reflect an excellent acceleration effect. As a meshless acceleration method, it is provided with enormous potentials for practice applications.