Seeking Truth and Beauty in Flavor Physics with Machine Learning
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Abstract (arXiv)
The discovery process of building new theoretical physics models involves the dual aspect of both fitting to the existing experimental data and satisfying abstract theorists' criteria like beauty, naturalness, etc. We design loss functions for performing both of those tasks with machine learning techniques. We use the Yukawa quark sector as a toy example to demonstrate that the optimization of these loss functions results in true and beautiful models.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 15 of its 37 references that are in the map. First posted on arXiv 31 Oct 2023.
Builds on
15 of 37 references.
References · 15
- 2019AI Feynman: a Physics-Inspired Method for Symbolic Regressioncited by 548
- 2016Interaction Networks for Learning about Objects, Relations and Physicscited by 509
- 2020Discovering Symbolic Models from Deep Learning with Inductive Biasescited by 344
- 2016A Compositional Object-Based Approach to Learning Physical Dynamicscited by 119
- 2018Discovering physical concepts with neural networkscited by 117
- 2022Rediscovering orbital mechanics with machine learningcited by 71
- 2020A Bayesian machine scientist to aid in the solution of challenging scientific problemscited by 61
- 2019Learning Symbolic Physics with Graph Networkscited by 53
- 2022Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discoverycited by 46
- 2022AI Poincar'e 2.0: Machine Learning Conservation Laws from Differential Equationscited by 20
- 2021Analytical Modelling of Exoplanet Transit Specroscopy with Dimensional Analysis and Symbolic Regressioncited by 18
- 2022Simplifying Polylogarithms with Machine Learningcited by 15
3 more
- 2022SYMBA: Symbolic Computation of Squared Amplitudes in High Energy Physics with Machine Learningcited by 13
- 2021Accelerating Understanding of Scientific Experiments with End to End Symbolic Regressioncited by 8
- 2022Is the Machine Smarter than the Theorist: Deriving Formulas for Particle Kinematics with Symbolic Regressioncited by 8
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Citing papers per year: 2024: 1; 2025: 1.
2 papers.