新型神经求解器应对欠阻尼 Langevin 动力学挑战

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-16 04:00

研究人员开发了一种新颖的迭代神经变分求解器，以应对计算欠阻尼 Langevin 动力学中承诺函数（committor function）的挑战。该新方法通过为向后 Kolmogorov 方程建立变分结构来克服现有数值技术的局限性，而这在此前对于欠阻尼系统是不可用的。该方法可以看作是广义的 Concus-Golub-Widlund 分裂方法，它利用神经网络高效地解决每个子问题，并在双势阱和 Muller-Brown 势等基准系统以及丙氨酸二肽上得到了验证。 AI

影响为模拟复杂物理系统中罕见的跃迁事件提供了一种新的计算工具，可能有助于化学和气候科学等领域的研究。

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在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Tiejun Li, Zhaoqi Liu, Xiang Zhou · 2026-10-16 04:00

欠阻尼Langevin动力学中承诺函数迭代神经变分求解器

arXiv:2610.20617v1 Announce Type: cross Abstract: The committor function describes the probability that a stochastic dynamical system, while transitioning between two metastable states, will first reach one state rather than the other. It is a central object in transition path th…

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报道来源 [1]

欠阻尼Langevin动力学中承诺函数迭代神经变分求解器

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