Learning to Emulate Chaos: Adversarial Optimal Transport Regularization
Chaos arises in many complex dynamical systems, from weather to power grids, but is difficult to accurately model with data-driven methods such as machine learning emulators. While emulators are promising tools for accelerating simulations and solving inverse problems, they still struggle to learn chaotic dynamics, where sensitivity to initial conditions renders exact long-term forecasts infeasible, especially given noisy data. Recent work instead trains emulators to match the statistical properties of chaotic attractors, but these approaches often rely on handcrafted summary statistics or large, diverse multi-environment datasets. In this work, we propose a family of adversarial optimal ...
| Authors | Gabriel Melo, Leonardo Santiago, Peter Y. Lu |
|---|---|
| Primary category | stat.ML |
| Published | 22 April 2026 |
| Last updated at source | 8 October 2026 |
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