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Physica D: Nonlinear Phenomena (1)

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Data-Driven Discovery of High-Dimensional Dynamical Systems with Sparse Interpretable Neural Networks

Siyuan Xing ^1,*, Qingyu Han², Efstathios G. Charalampidis ^3,4, and Ying-Cheng Lai ⁵

¹Mechanical Engineering Department, California Polytechnic State University, San Luis Obispo, California 93407-0403, USA
²Electrical Engineering Department, California Polytechnic State University, San Luis Obispo, California 93407-0403, USA
³Department of Mathematics and Statistics, San Diego State University, San Diego, California 92182-7720, USA
⁴Computational Science Research Center, San Diego State University, San Diego, California 92182-7720, USA
⁵School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona 85287, USA

^*Contact author: sixing@calpoly.edu

Machine learning for discovery of governing equations in physics: From sparse regression to neural operators and Koopman methods

Ali Tozar

Physica D: Nonlinear Phenomena 499 135473 (2027)

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