Author

Jaesung Choi

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Jul 2026

Extrapolating the emergence of Hamiltonian chaos with random-feature Hamiltonian neural networks

This is the first demonstration that a learned Hamiltonian can qualitatively extrapolate from predominantly regular dynamics into a broad chaotic sea absent from training, and what decides parameter extrapolation is not Hamiltonian structure alone but how the fitted Hamiltonian continues in the control parameter.

Jaesung Choi · arXiv.org · 0 citations

Preprint Aug 2026

Learning a quantitative criterion for distinguishing chaos from noise

It is demonstrated that the squared Pearson correlation coefficient provides a simple quantitative criterion for distinguishing chaos from noise directly from observed time-series data.

Jaesung Choi, Athokpam Langlen Chanu, Jong-Min Park · 0 citations