  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.
