arXiv Machine Learning By Pongpisit Thanasutives, Yoshinobu Kawahara

Robust data-driven discovery of fractional differential equations via weak formulations and Pareto-based subset selection

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arXiv:2608. 12879v1 Announce Type: new Abstract: Fractional partial differential equations describe nonlocal dynamics, but discovering them from noisy data is difficult because fractional differentiation amplifies high-frequency measurement noise and the derivative orders are unknown.

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arXiv Machine Learning
Sep 17

Learning Fractional-Order Dynamics from a Single Trajectory

arXiv:2609. 18127v1 Announce Type: new Abstract: Many real-world processes exhibit long-range dependence, where the current state depends on a slowly decaying trace of past states rather than on the most recent state alone.

By Xiaole Zhang, Ziyi Zhang, Zehao Zhao, Stephen Tu, Guannan Qu, Yorie Nakahira, Paul Bogdan