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.

摘要

复杂动力系统的神经网络模型通常不会明确说明结构工程洞察力和与此类系统的多物理性质相关的各种子过程的相互关系。出于这个原因，它们通常被解释为一种数据驱动的黑盒建模选项，与基于物理启发的基于方程的系统表示相反，随后在灰盒意义上确定了合适的参数。为了弥合数据驱动和基于方程的建模范式之间的差距，本文提出了一种受物理学启发的神经网络结构的新方法。这种结构的推导，针对高温燃料电池的热和电化学行为，证明了网络输入和隐藏层中神经元数量的最佳选择以及可实现的建模精度。最后，将不同的网络结构与实验数据进行比较。