English(EN) First-Order Trajectory Matching: Fast Ensemble Predictions of Chaotic, Turbulent, Stochastic Systems

新的FTM方法为复杂系统提供快速、准确的预测

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-09 17:29

研究人员开发了一种名为一阶轨迹匹配（FTM）的新代理建模方法，用于预测混沌、湍流和随机系统。FTM直接从系统轨迹中学习概率质量的局部传输，使其能够捕获通量和环流等基本量。这种方法避免了漂移、扩散和分数估计等复杂估计，以低计算成本提供稳定高效的集成预测。 AI

影响引入了一种改进混沌和随机系统预测的新方法，可能影响科学模拟和预测。

排序理由这是一篇详细介绍预测复杂系统新方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv cs.LG TIER_1 English(EN) · Shreya Jha, Timo Schorlepp, Nicholas Geissler, Jules Berman, Benjamin Peherstorfer · 2026-06-10 04:00

First-Order Trajectory Matching: Fast Ensemble Predictions of Chaotic, Turbulent, Stochastic Systems

arXiv:2606.11138v1 Announce Type: new Abstract: We introduce First-Order Trajectory Matching (FTM), a surrogate-modeling method that learns the first-order local transport of probability mass from trajectories of stochastic systems. By matching the symmetric first-order motion of…
arXiv cs.LG TIER_1 English(EN) · Benjamin Peherstorfer · 2026-06-09 17:29

First-Order Trajectory Matching: Fast Ensemble Predictions of Chaotic, Turbulent, Stochastic Systems

We introduce First-Order Trajectory Matching (FTM), a surrogate-modeling method that learns the first-order local transport of probability mass from trajectories of stochastic systems. By matching the symmetric first-order motion of trajectories, FTM learns the probability curren…