Fields
Pick a domain and keep going deeper: field, subfield, topic. Every step lists the papers at that point or below it. The counts are papers that are accepted or in review; papers that were not accepted are in the full library.
Field
Machine learning for physics (statistical, quantum many-body, particle, nonlinear dynamics, cosmology)
ML-for-physics work uses learning either as a tool inside a physics calculation (variational wave functions, generative samplers, simulation-based inference) or as a classifier / forecaster of physical data (phases of matter, jets, chaotic trajectories, galaxy surveys). Results are judged against exact or controlled references where they exist (exact diagonalisation, DMRG, Onsager solution, Monte Carlo truth labels) and by physics-specific metrics such as relative energy error, background rejection at fixed signal efficiency, valid prediction time in Lyapunov units, and calibrated posteriors on physical parameters.