A new framework integrates data assimilation and information-theoretic diagnostics to identify latent precursors, pathways, and mechanisms of extreme events in partially observed stochastic dynamical systems.
Extreme event prediction with multi-agent reinforcement learning-based parametriza- tion of atmospheric and oceanic turbulence
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A CNN surrogate with temporal coarse-graining accelerates 10-day advection simulations up to 92x while achieving r² of 0.60-0.98 against the baseline solver.
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Acceleration of horizontal numerical advection for atmospheric modeling through surrogate modeling with temporal coarse-graining
A CNN surrogate with temporal coarse-graining accelerates 10-day advection simulations up to 92x while achieving r² of 0.60-0.98 against the baseline solver.