AI RESEARCH
Coordinate Encoding on Linear Grids for Physics-Informed Neural Networks
arXiv CS.LG
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ArXi:2603.22700v1 Announce Type: new In solving partial differential equations (PDEs), machine learning utilizing physical laws has received considerable attention owing to advantages such as mesh-free solutions, unsupervised learning, and feasibility for solving high-dimensional problems. An effective approach is based on physics-informed neural networks (PINNs), which are based on deep neural networks known for their excellent performance in various academic and industrial applications. However, PINNs struggled with model