The term NeuralODE describes the structural combination of an Artifical Neural Network (ANN) and a numerical solver for Ordinary Differential Equations (ODEs), the former acts as the right-hand side of the ODE to be solved. This concept was further extended by a black-box model in the form of a Functional Mock-up Unit (FMU) to obtain a subclass of NeuralODEs, named NeuralFMUs. The resulting structure features the advantages of first-principle and data-driven modeling approaches in one single simulation model: A higher prediction accuracy compared to conventional First Principle Models (FPMs), while also a lower training effort compared to purely data-driven models. We present an intuitive workflow to setup and use NeuralFMUs, enabling the encapsulation and reuse of existing conventional models exported from common modeling tools. Moreover, we exemplify this concept by deploying a NeuralFMU for a consumption simulation based on a Vehicle Longitudinal Dynamics Model (VLDM), which is a typical use case in automotive industry. Related challenges that are often neglected in scientific use cases, like real measurements (e.g. noise), an unknown system state or high-frequent discontinuities, are handled in this contribution. For the aim to build a hybrid model with a higher prediction quality than the original FPM, we briefly highlight two open-source libraries: FMI.jl for integrating FMUs into the Julia programming environment, as well as an extension to this library called FMIFlux.jl, that allows for the integration of FMUs into a neural network topology to finally obtain a NeuralFMU.
翻译:Neurorode 描述人工神经网络(ANN)和普通差异模型(ODE)的数字解析器的结构组合。 Neurorode 是指一个人工神经网络(ANN)和普通差异模型(ODE)的结构性组合,前者作为要解决的 ODE 的右侧。这个概念通过一个黑箱模型进一步扩展,其形式为功能模拟股(FMU),以获得一个名为NeuroroFMU的子类神经模型。由此形成的结构具有在单一模拟模型中采用第一原理和数据驱动模型的优势:与传统的一原则模型(FPMMS)相比,预测准确度更高,而与纯数据驱动模型相比,前者则是更低调的培训努力。我们提出了一个直观的工作流程,以设置和使用NeurorFMUMU(FMMMU)为主。此外,我们通过部署NeuralFFFFMMMU来展示这一概念,这是汽车工业的一个典型应用案例。相关挑战往往被忽略,在科学应用的系统中,例如对FMULMULMU的升级进行高分辨率的整合。我们的系统进行不透明的整合。