DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systems

no code implementations • 6 Feb 2024 • Yair Schiff, Zhong Yi Wan, Jeffrey B. Parker, Stephan Hoyer, Volodymyr Kuleshov, Fei Sha, Leonardo Zepeda-Núñez

Learning dynamics from dissipative chaotic systems is notoriously difficult due to their inherent instability, as formalized by their positive Lyapunov exponents, which exponentially amplify errors in the learned dynamics.

Evolve Smoothly, Fit Consistently: Learning Smooth Latent Dynamics For Advection-Dominated Systems

no code implementations • 25 Jan 2023 • Zhong Yi Wan, Leonardo Zepeda-Núñez, Anudhyan Boral, Fei Sha

We present a data-driven, space-time continuous framework to learn surrogate models for complex physical systems described by advection-dominated partial differential equations.

Data-assisted reduced-order modeling of extreme events in complex dynamical systems

1 code implementation • 9 Mar 2018 • Zhong Yi Wan, Pantelis R. Vlachas, Petros Koumoutsakos, Themistoklis P. Sapsis

In this way, the data-driven model improves the imperfect model in regions where data is available, while for locations where data is sparse the imperfect model still provides a baseline for the prediction of the system dynamics.

Chaotic Dynamics Computational Physics