Physics-informed neural networks for multi-field visualization with single-color laser induced fluorescence

TL;DR

This paper demonstrates that physics-informed neural networks can effectively reconstruct temperature, velocity, and pressure fields from sparse, noisy experimental data, with transfer learning significantly reducing computation time.

Contribution

It introduces the application of PINNs to multi-field reconstruction from experimental data and shows transfer learning can accelerate this process while maintaining accuracy.

Findings

PINNs effectively denoise experimental data conforming to physics laws.

Transfer learning reduces training time by a factor of 9.9 with minimal error increase.

Reconstructed fields closely match PIV and FVM reference data.

Abstract

Reconstructing fields from sparsely observed data is an ill-posed problem that arises in many engineering and science applications. Here, we investigate the use of physics-informed neural networks (PINNs) to reconstruct complete temperature, velocity and pressure fields from sparse and noisy experimental temperature data obtained through single-color laser-induced fluorescence (LIF). The PINNs are applied to the laminar mixed convection system, a complex but fundamentally important phenomenon characterized by the simultaneous presence of transient forced and natural convection behaviors. To enhance computation efficiency, this study also explores transfer learning (TL) as a mean of significantly reducing the time required for field reconstruction. Our findings demonstrate that PINNs are effective, capable of eliminating most experimental noise that does not conform to governing physics laws. Additionally, we show that the TL method achieves errors within 5% compared to the regular training scheme while reducing computation time by a factor of 9.9. We validate the PINN reconstruction results using non-simultaneous particle image velocimetry (PIV) and finite volume method (FVM) simulations. The reconstructed velocity fields from the PINN closely match those obtained from PIV. When using FVM data as a reference, the average temperature errors are below 1%, while the pressure and velocity errors are below 10%. This research provides insights into the feasibility of using PINNs for solving ill-posed problems with experimental data and highlights the potential of TL to enable near real-time field reconstruction.