Physics-informed time series analysis with Kolmogorov-Arnold Networks under Ehrenfest constraints

arXiv:2509.18483 · cs.LG, quant-ph · Submitted 2025-09-23 · Read on arXiv

cs.LG, quant-ph

Submitted: 2025-09-23

Updated: 2025-09-23

Journal ref: Phys. Rev. Research 8, 023018 (2026)

DOI: 10.1103/84vm-tvzm

Code: https://github.com/Blealtan/efficient-kan

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

Terminology

• Accuracy — 24× in this paper · explained in 1 episode
• Loss Function — 8× in this paper · explained in 2 episodes
• Activation Function — 6× in this paper · explained in 1 episode
• Temporal Convolution Network — 5× in this paper
• Interpretability — 4× in this paper · explained in 15 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Failure Modes — 3× in this paper · explained in 3 episodes
• Inverse Problem — 3× in this paper · explained in 2 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Computational overhead — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Hilbert Space — 2× in this paper · explained in 2 episodes
• Army Research Office — 1× in this paper
• Convolutional Neural Networks (CNNs) — 1× in this paper · explained in 2 episodes
• Dynamic Modeling — 1× in this paper · explained in 3 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Kolmogorov-Arnold Networks (KANs) — 1× in this paper · explained in 3 episodes
• Physical Constraints — 1× in this paper · explained in 7 episodes
• Physics-Informed Neural Networks (PINNs) — 1× in this paper · explained in 6 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Regularization — 1× in this paper · explained in 3 episodes

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