Summary of Solving Differential Equations Using Physics-informed Deep Equilibrium Models, by Bruno Machado Pacheco and Eduardo Camponogara
Solving Differential Equations using Physics-Informed Deep Equilibrium Models
by Bruno Machado Pacheco, Eduardo Camponogara
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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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 introduces Physics-Informed Deep Equilibrium Models (PIDEQs) for solving initial value problems (IVPs) of ordinary differential equations (ODEs). By combining recent advancements in deep equilibrium models (DEQs) and physics-informed neural networks (PINNs), PIDEQs leverage the implicit output representation of DEQs with physics-informed training techniques. The paper validates PIDEQs using the Van der Pol oscillator as a benchmark problem, demonstrating their efficiency and effectiveness in solving IVPs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates new models called Physics-Informed Deep Equilibrium Models (PIDEQs) to help solve some math problems about how things change over time. These models are special because they use ideas from both deep learning and physics. The paper tests these models using a specific problem, the Van der Pol oscillator, and shows that they work well. |
Keywords
» Artificial intelligence » Deep learning
