English(EN) Goal-oriented learning of stochastic differential equations using error bounds on path-space observables

新方法提高了路径依赖可观测量SDE代理模型的准确性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-02 04:00

本文介绍了一种用于学习随机微分方程（SDE）代理模型的新型变分损失函数。所提出的面向目标学习方法使用路径空间可观测量上的误差界，而标准损失函数通常无法提供这一点。该方法被证明可以提高预测首次命中时间等统计数据的准确性，并且对于数据分布的偏移表现出鲁棒性，特别是对于过阻尼Langevin系统。 AI

影响为SDE代理模型引入了一种新颖的损失函数，有可能提高各种应用中模拟的效率和准确性。

排序理由这是一篇研究论文，详细介绍了一种学习SDE代理模型的新方法。[lever_c_demoted from research: ic=1 ai=0.7]

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arXiv cs.LG TIER_1 English(EN) · Joanna Zou, Han Cheng Lie, Youssef Marzouk · 2026-07-02 04:00

使用路径空间可观测量上的误差界限进行随机微分方程的面向目标学习

arXiv:2603.20467v2 Announce Type: replace-cross Abstract: Stochastic differential equations (SDEs), which serve as the governing equations for dynamical systems in a broad range of applications, can become cost-prohibitive for numerical simulation at scales necessary for quantify…