When: 15-27 June 2025
Where: KTH Main campus, Stockholm
More information can be found on PINNs website: https://pinns.se/
Teachers:
- Khemraj Shukla, Brown University
- Jennifer Ryan, KTH
- Stefano Markidis, KTH
- Matthieu Barreau, KTH
- Niccolò Tonicello, SISSA
Main Organizer: Kateryna Morozovska
Organizers and TAs:
- Federica Bragone, KTH
- Giuseppe Alessio D’Inverno, SISSA
- Edoardo Monti, Imperial College London & Toyota-Motors Europe
- Dario Coscia, SISSA & University of Amsterdam
- Zlatan Dimitrov, Sofia University
- Orfeas Bourchas, NTUA
- Andreas Panagiotopoulos, Max Planck Institute
Description: The program offers in-depth, hands-on experience in “Physics-Informed Neural Networks and Applications,” including interactive lectures, project work, and collaboration with top researchers in the field.
The course syllabus covers a variety of topics:
- Introduction to Deep Learning Networks
- Neural Network
- TensorFlow, PyTorch, JAX
- Discovery of differential equations
- Physics-Informed Neural Networks (advanced)
- DeepONet
- {DeepXDE} or {MODULUS}
- Uncertainty quantification
- Multi-GPU machine learning
- GPU clusters and multi-GPU programming.
- Learning on Graphs and Graph Neural Networks
More information can be found on PINNs website: https://pinns.se/
