Summary of Recovering Implicit Physics Model Under Real-world Constraints, by Ayan Banerjee and Sandeep K.s. Gupta
Recovering implicit physics model under real-world constraints
by Ayan Banerjee, Sandeep K.S. Gupta
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 abstract proposes a novel neural network architecture called liquid time constant neural network (LTC-NN) for recovering physics-driven models from real-world data. The existing methods either rely on high sampling rates or require explicit measurements, and assume known timestamps of external perturbations without uncertainty. The proposed LTC-NN architecture overcomes these limitations by using automatic differentiation, input-dependent time constants, and a physics model solver-based data reconstruction loss. Experiments show that LTC-NN is more accurate than state-of-the-art approaches in recovering implicit physics model coefficients on four benchmark dynamical systems, including three with simulation data and one with real-world data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to figure out the rules of physical systems from real-world data. Most current methods require lots of data or precise timing information, which isn’t always possible. The researchers created a special kind of neural network that can work with imperfect data and find the underlying rules of the system. They tested this method on several examples and showed that it works better than other approaches. |
Keywords
» Artificial intelligence » Neural network
