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

German

Objektation ist das Verständnis und die Kontrolle, wie Systeme sich entwickeln, ist zentral für Wissenschaft und Ingenieurwesen, wo viele Probleme das Argumentieren über komplette Bahnen erfordern. Bestehende Ansätze lernen typischerweise lokale Dynamik oder generieren Bahnen, aber Ziele, die aus einzelnen Übergängen konstruiert werden, können Abhängigkeiten im Laufe der Zeit verpassen, während generative Modelle nicht notwendigerweise ein behandelbares Ziel für die Bewertung und Optimierung willkürlicher Wege bieten. Wir studieren $\textit{action learning}$, das Problem der Wiederherstellung einer stochastischen Aktion aus Bahnbeobachtungen, was ein gemeinsames Ziel für die Argumentation über dynamische Systeme ergibt. Wir führen $\textbf{LEAP}$ ($\textb

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.

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

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