Newest papers

Action Learning for Scientific Simulations

Sarah Liaw, Yuanqi Du, Yilun Du · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.31585 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

既有的方法通常学习本地动态或生成轨道,但从个别过渡中构建的目标可以通过时间来错过依赖,而生成模型并不一定为评估和优化任意路径提供可处理的目标。我们研究了从轨道观察中恢复一个斯托加斯特行动的问题,从而产生了关于动态系统的推理的共同目标。我们引入了 $\textbf{LEAP}$($\textbf{L}$earned $\textbf{E}$nergy on LE inference $\textbf{A}$同样的轨道系统),该问题将未知的本地动作定位为可追踪的最佳化过程,并通过模拟和跨越时空和跨越时空的观察和跨越时空的观察,

Machine translation (Meta M2M100); the English original follows.

Abstract

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.

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

Topics: Computer science, Inference, Trajectory, Action (physics), Path (computing), Process (computing)

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange