Adaptive Neural Network Basis Methods for Solving Partial Differential Equations with Low-Regularity Solutions in Two and Three Dimensions

This paper proposes a novel adaptive neural network basis method (ANNB) for efficiently solving second-order partial differential equations (PDEs) with low-regularity solutions, leveraging domain decomposition, multi-scale neural networks, and residual-based adaptation to achieve high accuracy in two and three dimensions.