Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems

2 code implementations • 3 Aug 2018 • Francisco J. Gonzalez, Maciej Balajewicz

In this work we propose a deep learning-based strategy for nonlinear model reduction that is inspired by projection-based model reduction where the idea is to identify some optimal low-dimensional representation and evolve it in time.

Computational Efficiency Model Predictive Control