By Marcell Stippinger 2025-05-13
The AI ambassadors of HUN-REN Wigner RCP are organizing a journal club. Everyone interested in physics informed neural networks (PINN) from HUN-REN Wigner RCP or other HUN-REN or Hungarian academic institutions are welcome. To get an overview of the event series, click here.
Todays paper
We finish discussing
- Raissi, M., P. Perdikaris and G.E. Karniadakis. „Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations”.
- 2019, Journal of Computational Physics 378: 686–707.
- https://doi.org/10.1016/j.jcp.2018.10.045
- Code compatible with latest tensorflow: https://github.com/stippingerm/PINNs/
We briefly look into a paper that used generative AI for analytic/symbolic calculations
- Weiguo Yin. „Exact Solution of the Frustrated Potts Model with Next-Nearest-Neighbor Interactions in One Dimension: An AI-Aided Discovery”.
- arXiv, 2025.
- https://doi.org/10.48550/arXiv.2503.23758
Presented by: Marcell Stippinger
How to join
It is preferred to read the selected paper in advance for a more in-depth discussion. For each paper we select a participant in advance who is asked to present the paper on the big srceen.
You can join our journal club
- in person: Department of Computational Sciences, HUN-REN Wigner RCP, Building 6, Floor 2
- via Zoom: Meeting ID: 846 7811 0320, Passcode: 947438