Machine Learning and the Real Space Renormalization Group

Machine Learning and the Real Space Renormalization Group

Explore the intersection of machine learning and the Renormalization Group in this 30-minute Physics Next talk from 2018. Delve into the Real-space Renormalization Group from an Information Theory perspective, examining concepts such as Mutual Information and their applications. Analyze test cases including the 2D Ising model and the dimer model, while investigating RG flow and critical exponents. Gain insights into the optimality of the Real Space Mutual Information (RSMI) approach as presented by Maciej Kock-Janusz from the Swiss Federal Institute of Technology in Zurich.