Variational Learning of Physical Intuition from a Few Observations: Charting Manifolds of Variational Physics

Abstract

Humans often generalize physical outcomes from few observations, a desirable capacity known as physical intuition. We show that it can be computationally approached through charting the manifolds of variational physics. We conceive a variational learning framework, where small neural networks trained from merely two or three examples delineate an observation-spanned solution manifold, enabling generalization in unseen scenarios. As demonstrated across classical and quantum regimes including strongly correlated molecules, generalization emerges far beyond the training conditions. This generalization is explained by a unified theory: the Euler-Lagrange operator must be stationary with respect to the observation parameters along the solution manifold, whose complexity predicts a critical network capacity below which generalization fails. Our framework establishes a principled route to solving families of variational problems from a few instances, and shows how physical intuition can be approached through harnessing variational physics.