Gradient Surgery for Physics-Informed Neural Networks

arXiv:2609.30966 · cs.LG · Submitted 2026-09-25 · Read on arXiv

cs.LG

Submitted: 2026-09-25

Updated: 2026-09-25

Code: https://github.com/ThomBors/gs_pinn

Terminology

• Benchmark — 16× in this paper · explained in 2 episodes
• PDE residual — 7× in this paper · explained in 2 episodes
• Multi-Task Deep Learning — 5× in this paper
• Accuracy — 4× in this paper · explained in 1 episode
• Aggregation — 4× in this paper · explained in 1 episode
• Alignment — 4× in this paper · explained in 1 episode
• Adam — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Bayesian Optimization — 2× in this paper · explained in 3 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Inverse Problem — 2× in this paper · explained in 2 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Physical Constraints — 2× in this paper · explained in 7 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Adaptive Weighting — 1× in this paper · explained in 1 episode
• Adaptive Weighting Schemes — 1× in this paper · explained in 1 episode
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Gradient Flow — 1× in this paper · explained in 2 episodes
• Homogeneity — 1× in this paper · explained in 2 episodes
• Mean Rank — 1× in this paper
• Mean Squared Error — 1× in this paper
• Physics-Informed Neural Networks (PINNs) — 1× in this paper · explained in 6 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes

Sources

• Unleashing the Power of Multi-Task Learning: A Comprehensive Survey Spanning Traditional, Deep, and Pretrained Foundation Model Eras

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