An Operator Learning Framework for Spatiotemporal Super-resolution of Scientific Simulations

arXiv:2311.02328v2 Announce Type: replace
Abstract: In numerous contexts, high-resolution solutions to partial differential equations are required to capture faithfully essential dynamics which occur at small spatiotemporal scales, but these solutions can be very difficult and slow to obtain using traditional methods due to limited computational resources. A recent direction to circumvent these computational limitations is to use machine learning techniques for super-resolution, to reconstruct high-resolution numerical solutions from low-resolution simulations which can be obtained more efficiently. The proposed approach, the …

abstract arxiv computational cs.lg differential dynamics framework resolution resources scientific simulations small solutions type