Researchers have developed a new data-driven framework to learn effective stochastic dynamics from limited observational data of complex multiscale systems. This approach models coupled stochastic differential equations and uses normalizing flows to represent the invariant distribution of unobserved fast dynamics. The framework is trained end-to-end by optimizing a penalized likelihood objective and includes a Bayesian variational inference procedure for uncertainty quantification. AI
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IMPACT Introduces a novel method for modeling complex systems, potentially improving scientific simulations and data analysis.
RANK_REASON The cluster contains an academic paper detailing a new methodology for learning complex dynamics. [lever_c_demoted from research: ic=1 ai=1.0]