Opening book details…
Can I read Reconstructing Turbulent Velocity and Pressure Fields from Under-resolved Noisy Particle Tracks Using Physics-informed Neural Networks on EtoBox?
Reconstructing Turbulent Velocity and Pressure Fields from Under-resolved Noisy Particle Tracks Using Physics-informed Neural Networks by Patricio Clark Di Leoni; Karuna Agarwal; Tamer A. Zaki; Charles Meneveau; Joseph Katz is a Engineering article available to read on EtoBox.
What is Reconstructing Turbulent Velocity and Pressure Fields from Under-resolved Noisy Particle Tracks Using Physics-informed Neural Networks about?
Volume-resolving imaging techniques are rapidly advancing progress in experimental fluid mechanics. However, reconstructing the full and structured Eulerian velocity and pressure fields from under-resolved and noisy particle tracks obtained experimentally remains a significant challenge. We adopt and characterize a method based on Physics-Informed Neural Networks (PINNs). In this approach, the network is regularized by the Navier-Stokes equations to interpolate the velocity data and simultaneously determine the pressure field. We compare this approach to the state-of-the-art Constrained Cost Minimization method . Using data from direct numerical simulations and various types of synthetically generated particle tracks, we show that PINNs are able to accurately reconstruct both velocity and pressure even in regions with low particle density and small accelerations. We analyze both the root-mean-square error of the reconstructions as well their energy spectra. PINNs are also robust against increasing the distance between particles and the noise in the measurements, when studied under synthetic and experimental conditions. Both the synthetic and experimental datasets used correspond to
Who reads Reconstructing Turbulent Velocity and Pressure Fields from Under-resolved Noisy Particle Tracks Using Physics-informed Neural Networks?
It is typically read by researchers, students, and practitioners in Engineering.
- Author
- Patricio Clark Di Leoni; Karuna Agarwal; Tamer A. Zaki; Charles Meneveau; Joseph Katz
- Publisher
- Springer Science and Business Media LLC
- Published
- 2023
- Language
- EN
- Field
- Engineering (Physical Sciences)