Summary of Bond Graphs For Multi-physics Informed Neural Networks For Multi-variate Time Series, by Alexis-raja Brachet et al.
Bond Graphs for multi-physics informed Neural Networks for multi-variate time series
by Alexis-Raja Brachet, Pierre-Yves Richard, Céline Hudelot
First submitted to arxiv on: 22 May 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 This paper proposes a new hybrid Artificial Intelligence technique, Physical-Informed Machine Learning (PIML), which leverages Bond Graphs and Message Passing Graph Neural Networks to integrate data and architecture biases in deep learning. The authors aim to address the challenges of existing PIML methods, which are mainly formulated as end-to-end learning schemes and lack adaptability to complex multi-physical and multi-domain phenomena. Specifically, they introduce a Neural Bond graph Encoder (NBgE) that produces multi-physics-informed representations for tasks such as multivariate time-series forecasting. The proposed approach is evaluated on two challenging physical systems – a Direct Current Motor and the Respiratory System – demonstrating its effectiveness in predicting complex system behavior. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special computer programs to learn from big datasets. It’s trying to find new ways to make these programs better by using information about how things move or behave in different fields, like physics. The researchers are working on a new method that combines two types of AI: one that looks at patterns in data and another that builds models. They’re testing this method on real-world problems, like predicting how a motor works or understanding how our lungs breathe. |
Keywords
» Artificial intelligence » Deep learning » Encoder » Machine learning » Time series
