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Physically motivated structuring and optimization of neural networks for multi-physics modelling of solid oxide fuel cells.
- Source :
- Mathematical & Computer Modelling of Dynamical Systems; Dec 2021, Vol. 27 Issue 1, p586-614, 29p
- Publication Year :
- 2021
-
Abstract
- Neural network models for complex dynamical systems typically do not explicitly account for structural engineering insight and mutual interrelations of various subprocesses that are related to the multi-physics nature of such systems. For that reason, they are commonly interpreted as a kind of data-driven, black box modelling option that is in opposition to a physically inspired equation-based system representation for which suitable parameters are subsequently identified in a grey box sense. To bridge the gap between data-driven and equation-based modelling paradigms, this paper proposes a novel approach for a physics-inspired structuring of neural networks. The derivation of this kind of structuring, an optimal choice of network inputs and numbers of neurons in a hidden layer as well as the achievable modelling accuracy are demonstrated for the thermal and electrochemical behaviour of high-temperature fuel cells. Finally, different network structures are compared against experimental data. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13873954
- Volume :
- 27
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Mathematical & Computer Modelling of Dynamical Systems
- Publication Type :
- Academic Journal
- Accession number :
- 154690256
- Full Text :
- https://doi.org/10.1080/13873954.2021.1990966