arXiv:2604.21097v1 Announce Type: new Abstract: Chaos arises in many complex dynamical systems, from weather to power grids, but is difficult to accurately model using data-driven emulators, including neural operator architectures. For chaotic systems, the inherent sensitivity to initial conditions makes exact long-term forecasts theoretically infeasible, meaning that traditional squared-error los
Learning to Emulate Chaos: Adversarial Optimal Transport Regularization
Gabriel Melo, Leonardo Santiago, Peter Y. Lu·arXiv stat.ML··1 min read
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