AI RESEARCH
Learning Mesh-Free Discrete Differential Operators with Self-Supervised Graph Neural Networks
arXiv CS.LG
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ArXi:2603.24641v1 Announce Type: new Mesh-free numerical methods provide flexible discretisations for complex geometries; however, classical meshless discrete differential operators typically trade low computational cost for limited accuracy or high accuracy for substantial per-stencil computation. We