Cautious conflict-free inverse gradient methods improve physics-informed neural network training via reduced gradient conflicts
Read the original at arxiv.org→arXiv:2610.31855v1 Announce Type: new Abstract: Partial differential equations (PDEs) are indispensable tools for modeling complex phenomena in physics and engineering. Physics-Informed Neural Networks (PINNs) have...
Original headline: "Cautious Conflict-Free Inverse Gradient Methods"
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- Oct 26, 04:00 UTC arXiv cs.LG lead source Cautious Conflict-Free Inverse Gradient Methods