Learning of Dynamical Process Models based on Data and Expert Knowledge

该项目关注的是从仿真模型（由T2提供）和过程仪表（由T1/M1提供）提供的输入/输出时间序列中学习动态过程模型。这些学习的模型将用于M3中基于模型的参数优化，并在M4中用于设计反馈控制器，一旦过程的过度仪表化减少，反馈控制器可以解释固有的观察不确定性和驱动噪声。我们的动力学模型必须预测所产生的几何形状以及产品的其他空间分布特征。这将通过采用基于网格的神经架构来实现，该架构使用图卷积神经网络（GCNN）中的消息传递来在整个网格中空间传播状态信息。这种网络架构已经被证明可以准确预测复杂有限元（FE）模拟的动态，同时评估成本相当低，允许在可行的计算时间内将这些模型用于数据驱动的优化技术（M3和M4）。然而，这样的动态模型（到目前为止）只能从仿真数据中训练，并且没有以参数或分布式执行器为条件;因此，不清楚长期预测精度是否足以用于随后的基于模型的过程参数或反馈控制器的优化。除了数据驱动的学习之外，还将利用有关过程物理学的专业知识和知识，以获得高质量的模型，即使可用的数据很少。除了从过程中获得的真实的数据，我们将利用几个虚拟数据源，从物理激励的模拟具有不同的保真度水平，我们将转移模型只从模拟数据学习到真实的世界使用sim-2-真实的技术。因此，我们将在这个项目中攻击以下研究挑战：（i）获得神经网格为基础的模型，是足够快的优化;（ii）通过适应真实的数据集而超越模拟数据的准确性;（iii）在分布式参数和/或致动器上调节动力学模型，同时保持这些模型;（iv）通过将学习方法与分析物理先验相结合来使用少量的训练样本;（v）将已经学习的模型转移到新的、稍微适应的过程结构。此外，该项目需要使用以下人工智能方法：几何深度学习，图形卷积神经网络（GCNN），递归神经网络（RNN），Meta学习，Sim-2-真实的传输，物理信息神经网络架构和变分推理。

This project is concerned with the learning of dynamical process models from the input/output time series delivered by the simulation models (provided by T2) and the process instrumentation (provided by T1/M1). These learned models will be used in M3 for model-based parameter optimisation, and in M4 for designing feedback controllers that can account for the inherent observation uncertainty and actuation noise once the over-instrumentation of the process is reduced. Our dynamics models have to predict the resulting geometry as well as additional spatially distributed features of the product. This will be achieved by employing a mesh-based neural architecture that uses message passing in a Graph Convolutional Neural Network (GCNN) to propagate the state information spatially throughout the mesh. Such network architectures have already been shown to predict accurately the dynamics of complex Finite Element (FE) simulations, while being considerably cheaper to evaluate, allowing a potential use of these models for data-driven optimisation techniques (M3 and M4) in a feasible amount of computation time. However, such dynamics models can (so far) only be trained from simulation data, and have not been conditioned on parameters or distributed actuators; it is, therefore, unclear whether the long-term prediction accuracy is sufficient for subsequent model-based optimisation of the process parameters or feedback controllers. Besides data-driven learning, expertise and knowledge about the physics of the process will be exploited in order to obtain high-quality models, even if few data are available. In addition to real data obtained from the process, we will leverage several virtual data sources from physically motivated simulations with different levels of fidelity, and we will transfer models learned solely from simulation data to the real world using sim-2-real techniques.We will therefore attack the following research challenges in this project: (i) obtain neural mesh-based models that are fast enough for optimisation; (ii) go beyond the accuracy of the simulated data by adapting to real datasets; (iii) condition dynamics models on distributed parameters and/or actuators, while maintaining those models; (iv) work with a low number of training samples by combining learning approaches with analytical physics priors; (v) transfer already learned models to new, slightly adapted, structures of the process. Further, this project requires the use of the following AI methods: Geometric Deep Learning, Graph Convolutional Neural Networks (GCNN), Recurrent Neural Networks (RNN), Meta Learning, Sim-2-Real Transfer, Physics-Informed Neural Network Architectures and Variational Inference.