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.
If you would like to know more about PINNS before joining, we recommend this article. Otherwise continue reading.
Suggested papers about Physics Informed Neural Networks
- 2025-04-03. 14:30 Cuomo, Salvatore, Vincenzo Schiano Di Cola, Fabio Giampaolo, Gianluigi Rozza, Maziar Raissi, és Francesco Piccialli. „Scientific Machine Learning Through Physics–Informed Neural Networks: Where We Are and What’s Next”.
- 2022, Journal of Scientific Computing 92, (3): 88.
- https://doi.org/10.1007/s10915-022-01939-z
- history, comparison of architectures, ex (short): ODE, diffusion-reaction, advection, Navier-Stokes, hyperbolic, quantum
- Raissi, M., P. Perdikaris, és 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
- PDE, Navier-Stokes, Burgers, with Github
- Karniadakis, George Em, Ioannis G. Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, és Liu Yang. „Physics-Informed Machine Learning”.
- 2021, Nature Reviews Physics 3, (6): 422–440.
- https://doi.org/10.1038/s42254-021-00314-5
- entry level too
- Monaco, Simone, és Daniele Apiletti. „Training Physics-Informed Neural Networks: One Learning to Rule Them All?”
- 2023, Results in Engineering 18: 101023.
- https://doi.org/10.1016/j.rineng.2023.101023
- compare training
- Chandrajit Bajaj, Luke McLennan, Timothy Andeen and Avik Roy: Recipes for when physics fails: recovering robust learning of physics informed neural networks
- 2023 Mach. Learn.: Sci. Technol. 4: 015013
- https://iopscience.iop.org/article/10.1088/2632-2153/acb416/meta
- robustness
- Kim, Dongjin, és Jaewook Lee. „A Review of Physics Informed Neural Networks for Multiscale Analysis and Inverse Problems”.
- 2024, Multiscale Science and Engineering 6 (1): 1–11.
- https://doi.org/10.1007/s42493-024-00106-w
- interesting problems
- Wang, Sifan, Shyam Sankaran, Hanwen Wang, és Paris Perdikaris. „An Expert’s Guide to Training Physics-Informed Neural Networks”.
- 2023, arXiv
- https://doi.org/10.48550/arXiv.2308.08468
- more theory, on boundary conditions
- Krishnapriyan, Aditi S, Amir Gholami, Shandian Zhe, Robert M Kirby, és Michael W Mahoney. „Characterizing Possible Failure Modes in Physics-Informed Neural Networks”
- 2021, Advances in Neural Information Processing Systems 34: 26548-26560
- https://proceedings.neurips.cc/paper/2021/hash/df438e5206f31600e6ae4af72f2725f1-Abstract.html
- more advanced, regularization
- De Ryck, Tim, és Siddhartha Mishra. „Numerical Analysis of Physics-Informed Neural Networks and Related Models in Physics-Informed Machine Learning”.
- 2024, Acta Numerica 33: 633–713.
- https://doi.org/10.1017/S0962492923000089
- error propagantion and similar advanced stuff
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