Instability-wave prediction in hypersonic boundary layers with physics-informed neural operators

Y Hao, PC Di Leoni, O Marxen, C Meneveau… - Journal of …, 2023 - Elsevier
Fast and accurate prediction of the nonlinear evolution of instability waves in high-speed
boundary layers requires specialized numerical algorithms, and augmenting limited …

[HTML][HTML] Studying turbulent flows with physics-informed neural networks and sparse data

S Hanrahan, M Kozul, RD Sandberg - … Journal of Heat and Fluid Flow, 2023 - Elsevier
Physics-informed neural networks (PINNs) have recently become a viable modelling method
for the scientific machine-learning community. The appeal of this network architecture lies in …

Interpretable structural model error discovery from sparse assimilation increments using spectral bias‐reduced neural networks: A quasi‐geostrophic turbulence test …

R Mojgani, A Chattopadhyay… - Journal of Advances in …, 2024 - Wiley Online Library
Earth system models suffer from various structural and parametric errors in their
representation of nonlinear, multi‐scale processes, leading to uncertainties in their long …

[HTML][HTML] Ensemble flow reconstruction in the atmospheric boundary layer from spatially limited measurements through latent diffusion models

A Rybchuk, M Hassanaly, N Hamilton, P Doubrawa… - Physics of …, 2023 - pubs.aip.org
Due to costs and practical constraints, field campaigns in the atmospheric boundary layer
typically only measure a fraction of the atmospheric volume of interest. Machine learning …

A comprehensive review of advances in physics-informed neural networks and their applications in complex fluid dynamics

C Zhao, F Zhang, W Lou, X Wang, J Yang - Physics of Fluids, 2024 - pubs.aip.org
Physics-informed neural networks (PINNs) represent an emerging computational paradigm
that incorporates observed data patterns and the fundamental physical laws of a given …

Improving depth uncertainty in plenoptic camera-based velocimetry

M Moaven, A Gururaj, V Raghav, B Thurow - Experiments in Fluids, 2024 - Springer
This work describes the development of a particle tracking velocimetry (PTV) algorithm
designed to improve three-dimensional (3D), three-component velocity field measurements …

Low-dimensional representation of intermittent geophysical turbulence with high-order statistics-informed neural networks (H-SiNN)

R Foldes, E Camporeale, R Marino - Physics of Fluids, 2024 - pubs.aip.org
We present a novel machine learning approach to reduce the dimensionality of state
variables in stratified turbulent flows governed by the Navier–Stokes equations in the …

Flow reconstruction over a SUBOFF model based on LBM-generated data and physics-informed neural networks

X Chu, W Guo, T Wu, Y Zhou, Y Zhang, S Cai, G Yang - Ocean Engineering, 2024 - Elsevier
Flow reconstruction from sparse velocity measurements (either from simulation or
experiment) is essential in the study of SUBOFF models for the purpose of developing …

Neural network complexity of chaos and turbulence

T Whittaker, RA Janik, Y Oz - The European Physical Journal E, 2023 - Springer
Chaos and turbulence are complex physical phenomena, yet a precise definition of the
complexity measure that quantifies them is still lacking. In this work, we consider the relative …

Investigation of a neural implicit representation tomography method for flow diagnostics

D Kelly, B Thurow - Measurement Science and Technology, 2024 - iopscience.iop.org
In this work, a new gridless approach to tomographic reconstruction of 3D flow fields is
introduced and investigated. The approach, termed here as FluidNeRF, is based on the …