English(EN) Underdamped Stochastic Optimal Transport with Landauer boundary conditions: machine learning and analytical solution

机器学习解决具有 Landauer 边界条件的复杂最优输运问题

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 05:00

研究人员开发了一种新颖的机器学习方法来解决欠阻尼随机过程的复杂最优输运问题。该方法在具有非线性边界条件的场景中特别有效，例如与 Landauer 比特擦除问题相关的场景。该技术通过操纵外部势能，将系统动力学从单峰分布引导至双峰分布，并将其数值结果与适用于过阻尼极限的微扰展开进行了基准测试。 AI

影响引入了一种新颖的机器学习技术，用于复杂的物理模拟，可能为统计力学和控制理论的新研究开辟道路。

排序理由该条目是一篇在 arXiv 上发表的学术论文，详细介绍了一种用于解决特定类别物理/控制理论问题的新机器学习方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Julia Sanders, Dario Lucente, Marco Baldovin, Paolo Muratore-Ginanneschi · 2026-11-02 05:00

Underdamped Stochastic Optimal Transport with Landauer boundary conditions: machine learning and analytical solution

arXiv:2610.41599v1 Announce Type: cross Abstract: Finding optimal transport protocols for underdamped stochastic processes is a difficult yet important problem in control theory. In this paper we develop a direct method, based on machine learning, to find the optimal solution of …