Learning to Emulate Chaos: Adversarial Optimal Transport Regularization
The authors propose adversarial optimal transport objectives to jointly learn high-quality summary statistics and a physically consistent emulator from a single noisy chaotic trajectory. This method trains an emulator to match the data's statistical distribution by enforcing an optimal transport cost, which ensures long-term statistical fidelity for emulating complex chaotic systems.