PINN gradient conflicts occur in distinct regimes (persistent directional, magnitude imbalance, or low/transient) that each favor different fixes, with per-loss adapters plus reweighting improving results on forward and multi-physics problems.
Ali Heydari, Craig A
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Loss reweighting is cast as an inverse problem that dynamically infers class weights to equalize per-class average losses under the Neural Collapse simplex ETF target.
PINNs with hard and soft boundary enforcement solve membrane form-finding PDEs to accuracy comparable with FEM, with hard-BC yielding smaller boundary errors.
An auto-adaptive sampling technique for PINNs is introduced and tested on Allen-Cahn equations to better resolve interfacial regions compared to residual-adaptive methods.
citing papers explorer
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Per-Loss Adapters for Gradient Conflict in Physics-Informed Neural Networks
PINN gradient conflicts occur in distinct regimes (persistent directional, magnitude imbalance, or low/transient) that each favor different fixes, with per-loss adapters plus reweighting improving results on forward and multi-physics problems.
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Rethinking Loss Reweighting for Imbalance Learning as an Inverse Problem: A Neural Collapse Point of View
Loss reweighting is cast as an inverse problem that dynamically infers class weights to equalize per-class average losses under the Neural Collapse simplex ETF target.
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Physics-informed neural networks for form-finding of unilateral membrane structures
PINNs with hard and soft boundary enforcement solve membrane form-finding PDEs to accuracy comparable with FEM, with hard-BC yielding smaller boundary errors.
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Auto-Adaptive PINNs with Applications to Phase Transitions
An auto-adaptive sampling technique for PINNs is introduced and tested on Allen-Cahn equations to better resolve interfacial regions compared to residual-adaptive methods.