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Reconstruction of the Turbulent Flow Field with Sparse Piv Measurements Using Physics-Informed Neural Networks
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Outgoing claims: the papers this work cites, builds on, or argues with.
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An Overview of Unconstrained Optimization
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A practical approach to flow field reconstruction with sparse or incomplete data through physics informed neural network
inferredcites →https://doi.org/10.1007/s10915-022-01939-zopenalex-extracted
Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next
inferredcites →https://doi.org/10.1007/s11831-023-09890-4openalex-extracted
Stiff-PDEs and Physics-Informed Neural Networks
inferredcites →https://doi.org/10.1007/s40745-020-00253-5openalex-extracted
A Comprehensive Survey of Loss Functions in Machine Learning
inferredcites →https://doi.org/10.1016/j.apm.2020.07.056openalex-extracted
A hybrid approach combining DNS and RANS simulations to quantify uncertainties in turbulence modelling
inferredcites →https://doi.org/10.1016/j.cma.2022.114823openalex-extracted
Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems
inferredcites →https://doi.org/10.1016/j.compfluid.2008.05.002openalex-extracted
Flow over periodic hills – Numerical and experimental study in a wide range of Reynolds numbers
inferredcites →https://doi.org/10.1016/j.compfluid.2023.106025openalex-extracted
Physics-informed neural networks for the Reynolds-Averaged Navier–Stokes modeling of Rayleigh–Taylor turbulent mixing
inferredcites →https://doi.org/10.1016/j.ijheatmasstransfer.2023.124392openalex-extracted
Physics-informed neural network-based surrogate model for a virtual thermal sensor with real-time simulation
inferredcites →https://doi.org/10.1016/j.jcp.2017.07.050openalex-extracted
Machine learning of linear differential equations using Gaussian processes
inferredcites →https://doi.org/10.1016/j.jcp.2018.10.045openalex-extracted
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
inferredcites →https://doi.org/10.1016/j.jcp.2020.109951openalex-extracted
NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations
inferredcites →https://doi.org/10.1016/j.mtcomm.2021.102719openalex-extracted
A physics-informed deep learning method for solving direct and inverse heat conduction problems of materials
inferredcites →https://doi.org/10.1016/j.neucom.2020.09.006openalex-extracted
Distributed learning machines for solving forward and inverse problems in partial differential equations
inferredcites →https://doi.org/10.1016/j.petrol.2021.109205openalex-extracted
Prediction of porous media fluid flow using physics informed neural networks
inferredcites →https://doi.org/10.1017/dce.2022.37openalex-extracted
Mean flow reconstruction of unsteady flows using physics-informed neural networks
inferredcites →https://doi.org/10.1017/jfm.2014.566openalex-extracted
A data-assimilation method for Reynolds-averaged Navier–Stokes-driven mean flow reconstruction
inferredcites →https://doi.org/10.1017/jfm.2016.615openalex-extracted
Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
inferredcites →https://doi.org/10.1017/jfm.2016.803openalex-extracted
Deep learning in fluid dynamics
inferredcites →https://doi.org/10.1063/5.0048909openalex-extracted
An interpretable framework of data-driven turbulence modeling using deep neural networks
inferredcites →https://doi.org/10.1063/5.0090050openalex-extracted
Physics-informed data based neural networks for two-dimensional turbulence
inferredcites →https://doi.org/10.1063/5.0095270openalex-extracted
Physics-informed neural networks for solving Reynolds-averaged Navier–Stokes equations
inferredcites →https://doi.org/10.1063/5.0149547openalex-extracted
Data-driven Reynolds-averaged turbulence modeling with generalizable non-linear correction and uncertainty quantification using Bayesian deep learning
inferredcites →https://doi.org/10.1088/1361-6501/aa574copenalex-extracted
Image pre-processing method for near-wall PIV measurements over moving curved interfaces
inferredcites →https://doi.org/10.1115/1.4050542openalex-extracted
Physics-Informed Neural Networks for Heat Transfer Problems
inferredcites →https://doi.org/10.1146/annurev-fluid-010518-040547openalex-extracted
Turbulence Modeling in the Age of Data
inferredcites →https://doi.org/10.2514/6.2015-2460openalex-extracted
Machine Learning Methods for Data-Driven Turbulence Modeling
inferredcites →https://doi.org/10.2514/6.2017-3626openalex-extracted
Data-driven augmentation of turbulence models for adverse pressure gradient flows
inferredcites →https://doi.org/10.4230/lipics.ecoop.2019.25openalex-extracted
Automatic differentiation in machine learning: a survey
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Machine Learning for Fluid Mechanics
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