Danish Rafiq

Enhancing computational efficiency in multiscale systems using deep learning of coordinates and flow maps

Multiscale systems are expensive to simulate because fast dynamics require small time-steps, while slow dynamics require long prediction horizons. We propose latent hierarchical time-stepping (L-HiTS), which combines nonlinear coordinate discovery with multiscale flow-map learning. A deep autoencoder first compresses t...