Summary of Learning Deep Dissipative Dynamics, by Yuji Okamoto and Ryosuke Kojima
Learning Deep Dissipative Dynamics
by Yuji Okamoto, Ryosuke Kojima
First submitted to arxiv on: 21 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY); Dynamical Systems (math.DS)
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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 A novel study addresses the challenge of ensuring the dissipativity of neural networks learned from time-series data, which is crucial for dynamical systems. The authors propose a differentiable projection to transform any dynamics represented by neural networks into dissipative ones and develop a learning method for the transformed dynamics. This approach strictly guarantees stability, input-output stability, and energy conservation of trained systems. The method is demonstrated through applications in robotic arms and fluid dynamics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows that it’s possible to make sure a type of system learned from data behaves in a certain way. The authors use a special kind of projection to turn any neural network into one that is dissipative, which means it will always behave in a stable way. This is important because many real-world systems need to be stable and predictable. The study also shows how this method works by applying it to robots and fluids. |
Keywords
» Artificial intelligence » Neural network » Time series
