PhysiNet: A Combination of Physics-based Model and Neural Network Model for Digital Twins

• URL: http://arxiv.org/abs/2106.14790v1
• Date: Mon, 28 Jun 2021 15:13:16 GMT
• Title: PhysiNet: A Combination of Physics-based Model and Neural Network Model for Digital Twins
• Authors: Chao Sun, Victor Guang Shi
• Abstract summary: This paper proposes 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. Experiments showed that the proposed hybrid model outperformed both the physics-based model and the neural network model.
• Score: 0.5076419064097732
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 moded 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 model was able to automatically combine the models and boost their prediction performance. Experiments showed that the proposed hybrid model outperformed both the physics-based model and the neural network model.

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