Summary of Multi-fidelity Physics Constrained Neural Networks For Dynamical Systems, by Hao Zhou et al.
Multi-fidelity physics constrained neural networks for dynamical systems
by Hao Zhou, Sibo Cheng, Rossella Arcucci
First submitted to arxiv on: 3 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 proposed Multi-Scale Physics-Constrained Neural Network (MSPCNN) tackles the challenge of incorporating data with different levels of fidelity into a unified latent space. This is achieved through a customised multi-fidelity autoencoder that maps input representations to various physical spaces, enabling the evaluation of physical constraints in low-fidelity fields during model training. The resulting methodology offers a trade-off between training efficiency and accuracy, while also reducing complexity compared to conventional physics-constrained models. Numerical results demonstrate improved prediction accuracy and noise robustness when introducing physical constraints in low-fidelity fields. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Physics-constrained neural networks are used to make predictions more accurate and reliable. The problem is that these models can be difficult to train, especially for big systems with lots of data. One way to solve this issue is by using a special kind of autoencoder that combines different levels of detail into one space. This allows the model to learn from both high- and low-detail data while still being able to use physical rules to make predictions. In this paper, scientists tested this new approach on two types of fluid dynamics problems and found that it worked well. |
Keywords
* Artificial intelligence * Autoencoder * Latent space * Neural network
