A hybrid finite element/neural network solver and its application to the Poisson problem
arXiv:2307.00947 · doi:10.1002/pamm.202300135
Abstract
We analyze a hybrid method that enriches coarse grid finite element solutions with fine scale fluctuations obtained from a neural network. The idea stems from the Deep Neural Network Multigrid Solver (DNN-MG), (Margenberg et al., J Comput Phys 460:110983, 2022; A neural network multigrid solver for the Navier-Stokes equations) which embeds a neural network into a multigrid hierarchy by solving coarse grid levels directly and predicting the corrections on fine grid levels locally (e.g. on small patches that consist of several cells) by a neural network. Such local designs are quite appealing, as they allow a very good generalizability. In this work, we formalize the method and describe main components of the a-priori error analysis. Moreover, we numerically investigate how the size of training set affects the solution quality.
References in corpus (2)
Cited by in corpus (3)
- DNN-MG: A Hybrid Neural Network/Finite Element Method with Applications to 3D Simulations of the Navier-Stokes Equations
- Preconditioned FEM-based Neural Networks for Solving Incompressible Fluid Flows and Related Inverse Problems
- A robust and stable hybrid neural network/finite element method for 2D flows that generalizes to different geometries