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As the real-time digital counterpart of a physical system or process, digitaltwins are utilized for system simulation and optimization. Neural networks areone way to build a digital twins model by using data especially when aphysics-based model is not accurate or even not available. However, for a newlydesigned system, it takes time to accumulate enough data for neural networkmodel and only an approximate physics-based model is available. To takeadvantage of both models, this paper proposed a model that combines thephysics-based model and the neural network model to improve the predictionaccuracy for the whole life cycle of a system. The proposed hybrid model(PhysiNet) was able to automatically combine the models and boost theirprediction performance. Experiments showed that the PhysiNet outperformed boththe physics-based model and the neural network model.

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