When Does Frequency Decomposition Benefit Physics-Informed Neural Networks? A Preliminary Ablation Study

arXiv:2608.24940 · cs.LG, cs.AI · Submitted 2026-08-24 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-08-24

Updated: 2026-08-24

Terminology

• Benchmark — 73× in this paper · explained in 2 episodes
• Ablation — 30× in this paper · explained in 1 episode
• PDE residual — 10× in this paper · explained in 2 episodes
• Accuracy — 9× in this paper · explained in 1 episode
• Ablation study — 8× in this paper · explained in 1 episode
• Activation Function — 8× in this paper · explained in 1 episode
• Adam — 6× in this paper · explained in 2 episodes
• Inverse Problem — 3× in this paper · explained in 2 episodes
• Adaptive Weighting — 2× in this paper · explained in 1 episode
• Conservation Laws — 2× in this paper · explained in 3 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Physics-Informed Neural Networks (PINNs) — 2× in this paper · explained in 6 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Failure Modes — 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
• Interpretability — 1× in this paper · explained in 15 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Uncertainty Quantification — 1× in this paper · explained in 8 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism
• Solving the wave equation with physics-informed deep learning
• Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
• NeuralPDE: Automating Physics-Informed Neural Networks (PINNs) with Error Approximations

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