Domain decomposition methods with Physics-informed neural networks for elliptic equations on manifolds
It provides a numerical approach for solving PDEs on manifolds, which is relevant for high-dimensional geometric problems, but the results are incremental as they combine existing techniques.
The paper proposes two domain decomposition methods for elliptic equations on compact Riemannian manifolds using physics-informed neural networks, validated on manifolds in dimensions 5 to 10.
We propose two numerical domain decomposition methods (DDMs) for elliptic equations on compact Riemannian manifolds, based on physics-informed neural networks (PINNs). Our approach incorporates the DDM technique for manifolds with the advantages of neural networks in high-dimensional settings. The proposed methods are validated through numerical experiments on various manifolds, both with and without boundary, in dimensions ranging from $5$ to $10$.