Postponed_[RIBF-ULIC-miniWS037] Combining Nuclear Theory and Machine Learning for fundamental studies and applications

Wako Main research building room #435 (RIKEN Wako Campus)

Wako Main research building room #435

RIKEN Wako Campus

Sota Yoshida (Utunomiya University)

We arrange the schedule again in October.


In recent years, remarkable progress has been made in combining machine learning with nuclear models. Some of these studies have a significant impact on physics at RIBF: The density functional theory and machine learning have been combined to predict the mass and lifetime of r-process elements, and the nuclear optical model and machine learning are combined to predict neutron-nucleus scattering. Thus, the combination of machine learning and nuclear modeling has great potential.
This mini-workshop will bring together the researchers to discuss how ML can predict physical quantities (mass, lifetime, and/or (n,γ) cross-section) related to the r-process more accurately and what experiments should be proposed to improve the prediction accuracy. We will also explore new opportunities for collaboration.

Contact (Masaaki Kimura)
  • Tuesday, 30 August
    • 13:20 13:30
      opening 10m
      Speaker: Masaaki Kimura (RIKEN Nishina Center)
    • 13:30 14:30
      Shell model + ML (temporary) 1h
      Speaker: Noritaka Shimizu (Center for Nuclear Study, University of Tokyo)
    • 14:30 15:20
      RPA(E1) + ML 50m
      Speaker: Tsunenori Inakura (Tokyo Tech)
    • 15:40 16:40
      Nuclear mass predictions with machine learning reaching the accuracy required by r-process studies 1h
      Speaker: Dr Haozhao Liang (The University of Tokyo)
    • 16:40 17:40
      Discussion & Buffer
  • Wednesday, 31 August
    • 10:00 11:10
      TBA 1h 10m
      Speaker: Prof. Yoshiyuki Kabashima
    • 11:10 12:00
      TBA 50m
      Speaker: Ryosuke Akashi
    • 13:00 13:40
      ML for nuclear reaction database 40m
      Speaker: Tsunenori Inakura (Tokyo Tech)
    • 13:40 14:30
      ML for Fission products 50m
      Speaker: Futoshi Minato (Japan Atomic Energy Agency)
    • 14:30 15:20
      Learning from what we had disposed and an accelerator to “Machine Learning + nuclear physics” 50m
      Speaker: Sota Yoshida (The university of Tokyo)
    • 15:40 16:35
      Discussion & Buffer