Stokesian processes : inferring Stokes flows using physics-informed Gaussian processes

Abstract

We develop a probabilistic Stokes flow framework, using physics informed Gaussian processes, which can be used to solve both forward/inverse flow problems with missing and/or noisy data. The physics of the problem, specified by the Stokes and continuity equations, is exactly encoded into the inference framework. Crucially, this means that we do not need to explicitly solve the Poisson equation for the pressure field, as a physically meaningful (divergence-free) velocity field will automatically be selected. We test our method on a simple pressure driven flow problem, i.e. flow through a sinusoidal channel, and compare against standard numerical methods (Finite Element and Direct Numerical Simulations). We obtain excellent agreement, even when solving inverse problems given only sub-sampled velocity data on low dimensional sub-spaces (i.e. 1 component of the velocity on 1 D domains to reconstruct 2 D flows). The proposed method will be a valuable tool for analyzing experimental data, where noisy/missing data is the norm.

Keywords

• physics informed machine learning
• Stokes flow
• inverse problems
• Gaussian processes