Instability-wave prediction in hypersonic boundary layers with physics-informed neural operators
Fast and accurate prediction of the nonlinear evolution of instability waves in high-speed
boundary layers requires specialized numerical algorithms, and augmenting limited …
boundary layers requires specialized numerical algorithms, and augmenting limited …
[HTML][HTML] Studying turbulent flows with physics-informed neural networks and sparse data
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 …
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 …
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
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 …
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 …
that incorporates observed data patterns and the fundamental physical laws of a given …
Improving depth uncertainty in plenoptic camera-based velocimetry
This work describes the development of a particle tracking velocimetry (PTV) algorithm
designed to improve three-dimensional (3D), three-component velocity field measurements …
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)
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 …
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 …
experiment) is essential in the study of SUBOFF models for the purpose of developing …
Neural network complexity of chaos and turbulence
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 …
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 …
introduced and investigated. The approach, termed here as FluidNeRF, is based on the …