Two-Scale Neural Networks for Partial Differential Equations with Small Parameters (Communications in Computational Physics, 2025)
We propose a two-scale neural network method for solving partial differential equations (PDEs) with small parameters using physics-informed neural networks(PINNs). We directly incorporate the small parameters into the architecture of neuralnetworks. The proposed method enables solving PDEs with small parameters in a simple fashion, without adding Fourier features or other computationally taxing searchesof truncation parameters. Various numerical examples demonstrate reasonable accuracy in capturing features of large derivatives in the solutions caused by small parameters.
Keywords: Two-scale neural networks; partial differential equations; small parameters; successive training.