Parametric Data-Driven Reduced Order Model using neural networks and manifold-based interpolation*

The simulation of many industrial processes can be prohibitively expensive, due to the complexity of the mathematical models used to describe them. This makes the control of such processes a challenging task, especially when the models are parametric. In light of this, we present a data-driven framework to build a parametric reduced-order model (ROM) through the combination of singular value decomposition (SVD) and neural networks. A manifold-based nonlinear interpolation is used to deal with the effect of the parameter change on the spatial modes. On the other hand, we train a neural network to resolve the dynamics of the system. The framework presented in this work is non-intrusive, i.e., it does not require access to the governing equations of the original model. The performance of the framework is evaluated on a process of convective drying of wood chip particles. Results show that the ROM developed can accurately predict the dynamics of the process for a new set of parameters.