StablePDENet: Enhancing Neural Operator Stability through Physics-Informed Residual-Sensitivity Regularization

arXiv:2601.06472 · cs.LG · Submitted 2026-01-10 · Read on arXiv

cs.LG

Submitted: 2026-01-10

Updated: 2026-08-26

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Adversarial Training — 25× in this paper · explained in 1 episode
• Accuracy — 21× in this paper · explained in 1 episode
• Robustness — 17× in this paper · explained in 5 episodes
• DeepONet — 9× in this paper · explained in 2 episodes
• Loss Function — 5× in this paper · explained in 2 episodes
• Adversarial Examples — 4× in this paper · explained in 2 episodes
• Generalization — 4× in this paper · explained in 13 episodes
• Inverse Problem — 4× in this paper · explained in 2 episodes
• Regularization — 4× in this paper · explained in 3 episodes
• Adversarial Attacks — 3× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Adversarial Robustness — 2× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Projected Gradient Descent — 2× in this paper
• Self-Supervised Learning — 2× in this paper · explained in 8 episodes
• Uncertainty Quantification — 2× in this paper · explained in 8 episodes
• Activation Function — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Composite Loss Function — 1× in this paper · explained in 2 episodes
• Cooperation — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Physical Constraints — 1× in this paper · explained in 7 episodes
• Transferability — 1× in this paper · explained in 4 episodes

Sources

• Explaining and Harnessing Adversarial Examples

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