Flow field tomography with uncertainty quantification using a Bayesian physics-informed neural network

TL;DR

This paper introduces a Bayesian physics-informed neural network approach for flow field tomography, improving reconstruction accuracy and uncertainty quantification by integrating physics-based regularization with neural networks.

Contribution

The work presents a novel Bayesian PINN method for flow field tomography that enhances reconstruction quality and provides uncertainty estimates without requiring boundary condition knowledge.

Findings

Bayesian PINNs outperform state-of-the-art algorithms in flow field reconstruction.

The approach effectively quantifies uncertainty in the reconstructed flow fields.

The method reveals sources of semi-convergence in noisy conditions.

Abstract

We report a new approach to flow field tomography that uses the Navier-Stokes and advection-diffusion equations to regularize reconstructions. Tomography is increasingly employed to infer 2D or 3D fluid flow and combustion structures from a series of line-of-sight (LoS) integrated measurements using a wide array of imaging modalities. The high-dimensional flow field is reconstructed from low-dimensional measurements by inverting a projection model that comprises path integrals along each LoS through the region of interest. Regularization techniques are needed to obtain realistic estimates, but current methods rely on truncating an iterative solution or adding a penalty term that is incompatible with the flow physics to varying degrees. Physics-informed neural networks (PINNs) are new tools for inverse analysis that enable regularization of the flow field estimates using the governing physics. We demonstrate how a PINN can be leveraged to reconstruct a 2D flow field from sparse LoS-integrated measurements with no knowledge of the boundary conditions by incorporating the measurement model into the loss function used to train the network. The resulting reconstructions are remarkably superior to reconstructions produced by state-of-the-art algorithms, even when a PINN is used for post-processing. However, as with conventional iterative algorithms, our approach is susceptible to semi-convergence when there is a high level of noise. We address this issue through the use of a Bayesian PINN, which facilitates comprehensive uncertainty quantification of the reconstructions, enables the use of a more intuitive loss function, and reveals the source of semi-convergence.