Examples of Machine Learning methods and applications in nuclear physics
- Machine learning for data mining: Oftentimes, it is necessary to be able to accurately calculate observables that have not been measured, to supplement the existing databases.
- Nuclear density functional theory: Energy density functional calibration involving Bayesian optimization and NN ML. A promising avenue for ML applications is the emulation of DFT results.
- Nuclear properties with ML: Improving predictive power of nuclear models by emulating model residuals.
- Effective field theory and A-body systems: Truncation errors and low-energy coupling constant calibration, nucleon-nucleon scattering calculations, variational calculations with ANN for light nuclei, NN extrapolation of nuclear structure observables
- Nuclear shell model UQ: ML methods have been used to provide UQ of configuration interaction calculations.