Thirty Years of Education and Research on Nuclear Many-Body Physics at the ECT*; from traditional Methods to Quantum Computing and Machine Learning

 

 

 

Machine learning and nuclear theory (my bias): Why?

  1. ML tools can help us to speed up the scientific process cycle and hence facilitate discoveries
  2. Enabling fast emulation for big simulations
  3. Revealing the information content of measured observables w.r.t. theory
  4. Identifying crucial experimental data for better constraining theory
  5. Providing meaningful input to applications and planned measurements
  6. ML tools can help us to reveal the structure of our models
  7. Parameter estimation with heterogeneous/multi-scale datasets
  8. Model reduction
  9. ML tools can help us to provide predictive capability
  10. Theoretical results often involve ultraviolet and infrared extrapolations due to Hilbert-space truncations
  11. Uncertainty quantification essential
  12. Theoretical models are often applied to entirely new nuclear systems and conditions that are not accessible to experiment