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Physics-constrained deep neural network method for estimating parameters in a redox flow battery. (arXiv:2106.11451v2 [physics.chem-ph] UPDATED)
March 7, 2022, 2:11 a.m. | QiZhi He, Panos Stinis, Alexandre Tartakovsky
cs.LG updates on arXiv.org arxiv.org
In this paper, we present a physics-constrained deep neural network (PCDNN)
method for parameter estimation in the zero-dimensional (0D) model of the
vanadium redox flow battery (VRFB). In this approach, we use deep neural
networks (DNNs) to approximate the model parameters as functions of the
operating conditions. This method allows the integration of the VRFB
computational models as the physical constraints in the parameter learning
process, leading to enhanced accuracy of parameter estimation and cell voltage
prediction. Using an experimental …
arxiv deep neural network flow network neural network physics
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