English(EN) Model reduction in time: Learning flow maps for reducing stochastic dynamical systems in time

新的FM-FTM方法将动力学系统预测速度提高了100倍

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-28 04:00

研究人员开发了一种名为流映射一阶轨迹匹配（FM-FTM）的新方法，以加速随机动力学系统的预测。该方法学习“流映射”，可以替代多个顺序时间积分步骤，从而显著加快集成预测。数值实验表明，FM-FTM在预测时间边际和集成统计数据方面保持准确性的同时，可以将预测速度提高两个数量级。 AI

影响加速科学模拟，可能在依赖复杂动力学系统的领域实现更快的研发。

排序理由该集群包含一篇详细介绍加速科学计算新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Shreya Jha, Benjamin Peherstorfer · 2026-10-28 04:00

模型时间缩减：学习流图以在时间上缩减随机动力学系统

arXiv:2610.35504v1 Announce Type: new Abstract: Predictions with dynamical systems can be computationally expensive---even with reduced models---because reaching future prediction times can require many sequential time integration steps. We propose Flow-Map First-Order Trajectory…