Leveraging Physics-Informed Neural Networks for Noise Reduction in Fluid Flow Data

02 June 2025, Version 2
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Noise removal from sensor data remains a significant challenge in experimental studies. Traditional approaches such as filters and smoothers are widely used but often lack a physics-based foundation. These methods typically require domain expertise and extensive trial-and-error tuning, and their effectiveness diminishes with increasing noise levels—frequently resulting in distortion of the original signal or derived quantities. Physics-Informed Neural Networks (PINNs) offer a promising alternative by embedding physical laws and governing equations directly into the learning process. In this study, we evaluate the potential of PINNs for noise removal in transport equations and compare their performance with conventional numerical methods, both with and without filtering. Our analysis focuses on synthetically generated subsonic and supersonic flow data from numerical simulations. Results demonstrate that PINNs can successfully reconstruct pressure fields from noisy velocity data, outperforming traditional methods, especially in high Reynolds number scenarios. Conventional approaches struggle to denoise velocity fields in supersonic flows and often yield nonphysical pressure distributions. To address this, we introduce an adaptive-weight and adaptive-viscosity PINN framework that enables robust pressure reconstruction in supersonic regimes. These findings underscore the superior capability of physics-informed models in handling noise, particularly in compressible flows with shocks, where traditional filters fail. This study highlights that physics-based filtering may be essential for accurate reconstruction in shock-dominated problems.

Keywords

PINNs
Physics-Informed Neural Network
compressible flows
noise filter
incompressible flows

Comments

Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.