As the real-time digital counterpart of a physical system or process, digital twins are utilized for system simulation and optimization. Neural networks are one way to build a digital twins model by using data especially when a physics-based model is not accurate or even not available. However, for a newly designed system, it takes time to accumulate enough data for neural network model and only an approximate physics-based model is available. To take advantage of both models, this paper proposed a model that combines the physics-based model and the neural network model to improve the prediction accuracy for the whole life cycle of a system. The proposed hybrid model (PhysiNet) was able to automatically combine the models and boost their prediction performance. Experiments showed that the PhysiNet outperformed both the physics-based model and the neural network model.
@article{arxiv.2106.14790,
title = {PhysiNet: A Combination of Physics-based Model and Neural Network Model for Digital Twins},
author = {Chao Sun and Victor Guang Shi},
journal= {arXiv preprint arXiv:2106.14790},
year = {2021}
}