Laboratory of AI for Physics (LAIP)

Group of physicists keen on applications and development of AI methods for theoretical, computational, and experimental physics

• We work on applications of AI techniques in the physics of fluids, nuclear and particle physics, and supergravity.
• In particular, we focus on the development of:
  • Deep learning models for studies of porous media
  • Machine learning models for Monte Carlo simulations of neutrino-nucleus scattering
  • Bayesian methods for the analysis of electron and neutrino scattering data
  • Hopfield model
  • PINN in Bayesian approach
• The list of papers obtained within the initiative

======

Seminar: almost every Thursday, 9:00, room 416

• 20.06.2024:
  • title: tba, speaker: tba
• 13.06.2024:
  • title: tba, speaker: tba
• 06.06.2024:
  • title: Diffusion Model for MC Simulations, speaker: L. Bonilla
• 23.05.2024:
  • title: Diffusion by Deep Learning, speaker: D. Strzelczyk
• 16.05.2024:
  • title: Empirical fits to inclusive electron-carbon scattering data obtained by deep-learning methods, speaker: B. Kowal
• 09.05.2024:
  • title: Image processing in associative memory. From Hopfield networks to HopfieldLayer, speaker: B. Domański
• 25.04.2024:
  • title: GANs for MC Generator of Events, speaker: L. Bonilla
• 18.01.2024:
  • title: Bayesian Neural Network C++ Library: Review, speaker: C. Juszczak
• 30.11.2023:
  • title: Hopfield model - neural network based associative memory, speaker: B. Domański
• 09.11.2023:
  • title: AI Feynman: A Physics-Inspired Method for Symbolic Regression, speaker: R. Durka
• 26.10.2023:
  • title: Physics Informed Neural Networks, speaker: K. M. Graczyk