Stochastic particle advection velocimetry (SPAV): theory, simulations, and proof-of-concept experiments
Particle tracking velocimetry (PTV) is widely used to measure time-resolved, three-
dimensional velocity and pressure fields in fluid dynamics research. Inaccurate localization …
dimensional velocity and pressure fields in fluid dynamics research. Inaccurate localization …
[HTML][HTML] Denoising image-based experimental data without clean targets based on deep autoencoders
F Gu, S Discetti, Y Liu, Z Cao, D Peng - Experimental Thermal and Fluid …, 2024 - Elsevier
Linear data-driven methods have demonstrated being effective denoising filters for image-
based techniques. On the downside, they are still challenged by conditions of extremely low …
based techniques. On the downside, they are still challenged by conditions of extremely low …
Pressure field estimation of single-phase flows across a tube bundle using physics-informed neural networks
This paper presents a methodology for estimating the pressure field from experimental
velocity fields using physics-informed neural networks (PINNs). The proposed model …
velocity fields using physics-informed neural networks (PINNs). The proposed model …
Periodically activated physics-informed neural networks for assimilation tasks for three-dimensional Rayleigh-Benard convection
We apply physics-informed neural networks to three-dimensional Rayleigh-Benard
convection in a cubic cell with a Rayleigh number of Ra= 10^ 6 and a Prandtl number of Pr …
convection in a cubic cell with a Rayleigh number of Ra= 10^ 6 and a Prandtl number of Pr …
Image Velocimetry using Direct Displacement Field estimation with Neural Networks for Fluids
E Magaña, FS Costabal, W Brevis - arxiv preprint arxiv:2501.18641, 2025 - arxiv.org
An important tool for experimental fluids mechanics research is Particle Image Velocimetry
(PIV). Several robust methodologies have been proposed to perform the estimation of …
(PIV). Several robust methodologies have been proposed to perform the estimation of …
Complete characterization of axisymmetric turbulent jet using background oriented schlieren and physics-informed neural network
YK Rudenko, NA Vinnichenko… - Heat Transfer …, 2025 - dl.begellhouse.com
Axisymmetric turbulent jet of hot air is completely reconstructed from the experimentally
measured temperature field using physics-informed neural network (PINN), which takes into …
measured temperature field using physics-informed neural network (PINN), which takes into …
[HTML][HTML] Physics-informed neural networks for high-resolution weather reconstruction from sparse weather stations
Background The accurate provision of weather information holds immense significance to
many disciplines. One example corresponds to the field of air traffic management, in which …
many disciplines. One example corresponds to the field of air traffic management, in which …
[PDF][PDF] from weather station observation using physic-informed AI
BK SAN - aircraftoperationslab.com
Data-driven AI models for weather forecasting are becoming more and more popular as they
allow to propose NWPs at very low computational cost, however they present some …
allow to propose NWPs at very low computational cost, however they present some …