arXiv (All)
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Gradient-Update Mismatch: Rethinking Conflict-Free Training of Physics-Informed Neural Networks
Training Physics-Informed Neural Networks (PINNs) requires jointly optimizing physics residual and initial/boundary condition loss terms, which often induce conflicting gradients. Gradient surgery methods mitigate this issue by constructing directions from loss-specific gradients to reduce conflict
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