Closure Learning for Nonlinear Model Reduction Using Deep Residual Neural Network
Closure Learning for Nonlinear Model Reduction Using Deep Residual Neural Network
Developing accurate, efficient, and robust closure models is essential in the construction of reduced order models (ROMs) for realistic nonlinear systems, which generally require drastic ROM mode truncations. We propose a deep residual neural network (ResNet) closure learning framework for ROMs of nonlinear systems. The novel ResNet-ROM framework consists of …