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AI Algorithm for Spontaneous Symmetry Breaking by Dale Srinivas is a document available to read on EtoBox.
This paper presents an AI-based algorithm that reformulates spontaneous symmetry breaking (SSB) in subatomic physics as a stochastic inference problem using Bayesian probability and variational calculus. It introduces a neural network parameterization to identify symmetry-broken ground states and critical transitions in quantum systems. The work integrates classical physics concepts with an AI-probabilistic framework to enhance understanding of phase transitions and vacuum selection.
- Author
- Dale Srinivas
- Language
- EN