Summary of On Instabilities in Neural Network-based Physics Simulators, by Daniel Floryan
On instabilities in neural network-based physics simulators
by Daniel Floryan
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); Chaotic Dynamics (nlin.CD)
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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 Machine learning models are trained to simulate physical systems, but they often produce unphysical or unstable results due to instabilities in the training process. A new study analyzes the origin of these instabilities when learning linear dynamical systems, and finds that they stem from uneven convergence rates, unlearnable directions, and weight initialization effects. The study also shows that adding synthetic noise during training can stabilize the learned simulator, but at the cost of biased dynamics. To mitigate these issues, the authors suggest strategies for each contributing factor. The research has implications for learning both discrete-time and continuous-time dynamics, as well as nonlinear systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary When scientists train computers to simulate how things move or change over time, they often run into a problem: the computer’s predictions don’t make sense. A new study looks at why this happens when trying to teach machines to understand simple physical systems. The researchers found that it’s because the way the machine learns is not very consistent, and sometimes it just can’t learn certain things. They also discovered that the initial settings for the machine’s “brain” have a big impact on how accurate its predictions are. To make things better, the authors suggest ways to fix these problems. This research helps us understand how machines can be taught to simulate complex phenomena like moving objects or changing temperatures. |
Keywords
» Artificial intelligence » Machine learning
