PhysiNet: A combination of physics‐based model and neural network model for digital twins
PhysiNet: A combination of physics‐based model and neural network model for digital twins
复制标题
PhysiNet：数字孪生的基于物理的模型和神经网络模型的结合
DOI：
10.1002/int.22798
复制
发表时间：
2021-06
影响因子：
7
通讯作者：
Chao Sun;Victor Guang Shi
中科院分区：
文献类型：
--
作者：
Chao Sun;Victor Guang Shi
作为物理系统或过程的实时数字对应物，数字孪生用于系统仿真和优化。神经网络是利用数据构建数字双胞胎模型的一种方法，特别是当基于物理的模型不准确甚至不可用时。然而，对于一个新设计的系统，需要时间来积累足够的神经网络模型数据，并且只有一个近似的基于物理的模型可用。为了充分利用这两种模型的优点，本文提出了一种基于物理模型和神经网络模型相结合的模型，以提高系统全生命周期的预测精度。所提出的混合模型（PhysiNet）能够自动组合模型并提高其预测性能。实验表明，PhysiNet的性能优于基于物理的模型和神经网络模型。
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 models 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.