In the trend of hybrid Artificial Intelligence (AI) techniques, Physic
Informed Machine Learning has seen a growing interest. It operates mainly by
imposing a data, learning or inductive bias with simulation data, Partial
Differential Equations or equivariance and invariance properties. While these
models have shown great success on tasks involving one physical domain such as
fluid dynamics, existing methods still struggle on tasks with complex
multi-physical and multi-domain phenomena. To address this challenge, we
propose to leverage Bond Graphs, a multi-physics modeling approach together
with Graph Neural Network. We thus propose Neural Bond Graph Encoder (NBgE), a
model agnostic physical-informed encoder tailored for multi-physics systems. It
provides an unified framework for any multi-physics informed AI with a graph
encoder readable for any deep learning model. 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
multi-variate time series forecasting task.

我们提出了一种结合了 Bond Graphs 和 Graph Neural Network 的神经物理信息编码器（NBgE），用于解决复杂的多物理领域任务。通过实验证明了该方法在多变量时间序列预测任务上的有效性。