We present generative artificial intelligence (AI) to empirically validate fundamental laws of physics, focusing on the Stefan-Boltzmann law linking stellar temperature and luminosity. Our approach simulates counterfactual luminosities under hypothetical temperature regimes for each individual star and iteratively refines the temperature-luminosity relationship in a deep learning architecture. We use Gaia DR3 data and find that, on average, temperature's effect on luminosity increases with stellar radius and decreases with absolute magnitude, consistent with theoretical predictions. By framing physics laws as causal problems, our method offers a novel, data-driven approach to refine theoretical understanding and inform evidence-based policy and practice.
Cite
@article{arxiv.2503.17894,
title = {Generative AI for Validating Physics Laws},
author = {Maria Nareklishvili and Nicholas Polson and Vadim Sokolov},
journal= {arXiv preprint arXiv:2503.17894},
year = {2025}
}