arXiv 论文提出 LEAP：从轨迹观测学习随机作用量
Action Learning for Scientific Simulations
一篇论文把物理轨迹数据变成可复用的目标函数，LEAP 在分子模拟和多体轨道上都验证了，做科学计算推理的可以看看。
一篇 arXiv 论文提出 action learning 问题，即从轨迹观测中恢复随机系统的动作。作者提出 LEAP（Learned Energy on Action Paths）方法，将未知动作锚定到可解的参考过程，通过对比观测轨迹与参考轨迹学习修正项。该方法在路径空间上给出可微目标，能捕捉局部动力学结构和跨时间的非可加依赖。在随机物理系统、多体轨道动力学和分子模拟三类任务上，LEAP 恢复了底层动力学，并支持路径排序、机制调控、约束优化与采样等多种推理目标间的迁移。
Action Learning for Scientific Simulations
Understanding and controlling how systems evolve is central to science and engineering, where many problems require reasoning about complete trajectories. Existing approaches typically learn local dynamics or generate trajectories, but objectives constructed from individual transitions can miss dependencies across time, while generative models do not necessarily provide a tractable objective for evaluating and optimizing arbitrary paths. We study $\textit{action learning}$, the problem of recovering a stochastic action from trajectory observations, yielding a common objective for reasoning about dynamical systems. We introduce $\textbf{LEAP}$ ($\textbf{L}$earned $\textbf{E}$nergy on $\textbf{A}$ction $\textbf{P}$aths), which anchors the unknown action to a tractable reference process and learns the remaining correction by comparing observed trajectories with reference trajectories. This construction yields a differentiable objective on path space that can capture both local dynamical structure and nonadditive dependencies across time. Once learned, the same action can be flexibly reused for path ranking and inference, mechanism steering, constrained optimization and sampling. Across stochastic physical systems, many-body orbital dynamics and molecular simulations, LEAP recovers underlying dynamics, captures dependencies across time and enables transfer across inference objectives and physical conditions. We show that $\textit{action learning}$ is a promising framework for turning trajectory observations into reusable objectives for scientific inference and control.
