Bond Graphs for multi-physics informed Neural Networks for multi-variate time series

In the trend of hybrid Artificial Intelligence techniques, Physical-Informed Machine Learning has seen a growing interest. It operates mainly by imposing data, learning, or architecture bias with simulation data, Partial Differential Equations, or equivariance and invariance properties. While it has...

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Bibliographic Details
Published in:arXiv.org 2024-08
Main Authors: Alexis-Raja Brachet, Pierre-Yves, Richard, Hudelot, Céline
Format: Article
Language:English
Subjects:
Artificial intelligence
Bond graphs
Coders
Deep learning
Direct current
Fluid dynamics
Graph neural networks
Machine learning
Neural networks
Partial differential equations
Physics
Respiratory system
Task complexity
Time series
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Summary:In the trend of hybrid Artificial Intelligence techniques, Physical-Informed Machine Learning has seen a growing interest. It operates mainly by imposing data, learning, or architecture bias with simulation data, Partial Differential Equations, or equivariance and invariance properties. While it has shown great success on tasks involving one physical domain, such as fluid dynamics, existing methods are not adapted to tasks with complex multi-physical and multi-domain phenomena. In addition, it is mainly formulated as an end-to-end learning scheme. To address these challenges, we propose to leverage Bond Graphs, a multi-physics modeling approach, together with Message Passing Graph Neural Networks. We propose a Neural Bond graph Encoder (NBgE) producing multi-physics-informed representations that can be fed into any task-specific model. It provides a unified way to integrate both data and architecture biases in deep learning. Our experiments on two challenging multi-domain physical systems - a Direct Current Motor and the Respiratory System - demonstrate the effectiveness of our approach on a multivariate time-series forecasting task.
ISSN:2331-8422