Statistical Patterns in the Equations of Physics and the Emergence of a Meta-Law of Nature
Not on the map. Why?
Abstract (arXiv)
Physics seeks to uncover the laws of Nature and express them through mathematical equations. Despite the vast diversity of natural phenomena, physical equations exhibit structural regularities that set them apart from arbitrary mathematical expressions. While principles such as dimensional analysis have long guided the formulation of physical models, the exploration of more subtle statistical patterns within the equations of physics remains an open question. Here, by analysing four corpora of physics equations and applying advanced implicit-likelihood techniques, we find that the frequency of mathematical operators follows an exponential decay law, in contrast to Zipf's power law for word frequencies in natural languages. This reveals a statistical meta-law of physics, possibly reflecting a combination of communication efficiency and constraints imposed by Nature itself. The meta-law offers practical benefits for symbolic regression by drastically narrowing down the space of physically plausible expressions. More broadly, it may inform the development of language models that can generate coherent mathematical representations, advancing the automation of physical law discovery.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 13 of its 59 references that are in the map. First posted on arXiv 12 Aug 2024.
Builds on
13 of 59 references.
References · 13
- 2019AI Feynman: a Physics-Inspired Method for Symbolic Regressioncited by 536
- 2019The frontier of simulation-based inferencecited by 490
- 2019Deep Learning for Symbolic Mathematicscited by 266
- 2021Neural Symbolic Regression that Scalescited by 185
- 2020A Bayesian machine scientist to aid in the solution of challenging scientific problemscited by 61
- 2023Sampling-Based Accuracy Testing of Posterior Estimators for General Inferencecited by 45
- 2022Exhaustive Symbolic Regressioncited by 29
- 2018Studying the Difference Between Natural and Programming Language Corporacited by 13
- 2023Evidence Networks: simple losses for fast, amortized, neural Bayesian model comparisoncited by 10
- 2024LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and Cosmologycited by 8
- 2023Optimal Inflationary Potentialscited by 6
- 2013Zipf's law and criticality in multivariate data without fine-tuningcited by 2
Built on by
Citing papers per year: 2024: 1; 2025: 0; 2026: 1.
2 papers.