Joint Parameter and Parameterization Inference with Uncertainty Quantification through Differentiable Programming

arXiv:2403.02215v1 Announce Type: new
Abstract: Accurate representations of unknown and sub-grid physical processes through parameterizations (or closure) in numerical simulations with quantified uncertainty are critical for resolving the coarse-grained partial differential equations that govern many problems ranging from weather and climate prediction to turbulence simulations. Recent advances have seen machine learning (ML) increasingly applied to model these subgrid processes, resulting in the development of hybrid physics-ML models through the integration with numerical solvers. In this work, we introduce a novel …

abstract advances arxiv climate cs.lg differentiable differential grid inference math.ds nlin.cd numerical physics.ao-ph prediction processes programming quantification simulations through turbulence type uncertainty weather