New framework learns complex multiscale dynamics using normalizing flows

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

影响 Introduces a novel method for modeling complex systems, potentially improving scientific simulations and data analysis.

排序理由 The cluster contains an academic paper detailing a new methodology for learning complex dynamics. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

• arXiv
• normalizing flows
• stochastic differential equations

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