Efficient Closure Learning in Dynamical Systems: A Comparison of Data-Assimilated and Embedded Approaches
Paper recorded by Signals 4 on 2026-11-02 in cs.LG. Abstract reproduced from arXiv; link to the original below.
Published 2026-11-02 on arXiv · recorded by Signals 4 on 2026-11-03
Category: cs.LG · 机器学习 · first seen 2026-11-03
Abstract
Increased computing power has transformed computational science and improved predictive capabilities for complex systems, but many phenomena still exceed current simulation capabilities or require rapid evaluations for fast design iterations. Closure models address this challenge by approximating unresolved effects in reduced or incomplete dynamical systems. Data-driven closures offer a flexible m
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Cite this page: Efficient Closure Learning in Dynamical Systems: A Comparison of Data-Assimilated and Embedded Approaches: the #21 most recent of 703 cs.LG papers we have recorded (as of 2026-11-02). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/efficient-closure-learning-in-dynamical-systems-a-comparison-of-data-assimilated.html
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