Summary of Pinnfluence: Influence Functions For Physics-informed Neural Networks, by Jonas R. Naujoks et al.
PINNfluence: Influence Functions for Physics-Informed Neural Networks
by Jonas R. Naujoks, Aleksander Krasowski, Moritz Weckbecker, Thomas Wiegand, Sebastian Lapuschkin, Wojciech Samek, René P. Klausen
First submitted to arxiv on: 13 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computational Physics (physics.comp-ph); Fluid Dynamics (physics.flu-dyn)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the application of influence functions (IFs) to validate and debug physics-informed neural networks (PINNs), which are a type of deep learning model used for solving partial differential equations in the physical sciences. Specifically, the authors apply variations of IF-based indicators to gauge the influence of different types of collocation points on the prediction of PINNs applied to a 2D Navier-Stokes fluid flow problem. The results demonstrate how IFs can be adapted to PINNs to reveal potential for further studies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper takes a type of deep learning model called physics-informed neural networks (PINNs) and uses something called influence functions (IFs) to make sure they’re working correctly. This helps solve big problems in physics, like how fluids move. They test it on a problem with fluid flow and show that this method can be useful. |
Keywords
» Artificial intelligence » Deep learning
