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

新的FTM方法以低成本预测混沌系统

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、湍流和随机系统的行为。FTM直接从系统轨迹中学习，以模拟概率质量的传输，从而以较低的计算成本实现准确的集成预测。通过将离散化误差与采样方差分离来分析该方法的稳定性，确保在时间分辨率和样本量平衡时获得可靠的结果。 AI

影响引入了一种有效预测复杂动力学系统的新颖方法，可能影响科学模拟和预测。

排序理由该集群包含一篇详细介绍新研究方法的学术论文。

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…