Dynamic Deep Learning Based Super-Resolution For The Shallow Water Equations

arXiv:2404.06400v1 Announce Type: new
Abstract: Using the nonlinear shallow water equations as benchmark, we demonstrate that a simulation with the ICON-O ocean model with a 20km resolution that is frequently corrected by a U-net-type neural network can achieve discretization errors of a simulation with 10km resolution. The network, originally developed for image-based super-resolution in post-processing, is trained to compute the difference between solutions on both meshes and is used to correct the coarse mesh every 12h. Our setup is the …

abstract arxiv benchmark cs.lg deep learning dynamic errors network neural network ocean physics.comp-ph physics.flu-dyn resolution simulation type water