Jin-Long Wu

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Jinlong Wu
Publication record
37 papers · 2,639 citations · H-index 18

PAPERS

Obtaining the mean fields with known Reynolds stresses at steady state
20201 citationsauthor #4
Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical systems
2019146 citationsauthor #1
Flows over periodic hills of parameterized geometries: A dataset for data-driven turbulence modeling from direct simulations
2019138 citationsauthor #2
Recent progress in augmenting turbulence models with physics-informed machine learning
201933 citationsauthor #2
Representation of stress tensor perturbations with application in machine-learning-assisted turbulence modeling
201947 citationsauthor #1
Physics-informed covariance kernel for model-form uncertainty quantification with application to turbulent flows
201924 citationsauthor #1
Enforcing Imprecise Constraints on Generative Adversarial Networks for Emulating Physical Systems
201920 citationsauthor #2
Seeing permeability from images: fast prediction with convolutional neural networks.
2018189 citationsauthor #1
Reynolds-averaged Navier–Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned
2018181 citationsauthor #1
Recognition of Permeability from Images with Convolutional Neural Networks
20181 citationsauthor #2
Data-Driven, Physics-Based Feature Extraction from Fluid Flow Fields using Convolutional Neural Networks
201870 citationsauthor #2
Predictive Turbulence Modeling with Bayesian Inference and Physics-Informed Machine Learning
20180 citationsauthor #1
Physics-Informed Generative Adversarial Networks by Incorporating Conservation Laws
20180 citationsauthor #2
Data-Driven Augmentation of Turbulence Models with Physics-Informed Machine Learning
20189 citationsauthor #1
Physics-Informed Generative Learning to Predict Unresolved Physics in Complex Systems
20180 citationsauthor #1
On the Conditioning of Machine-Learning-Assisted Turbulence Modeling
20170 citationsauthor #1
Visualization of High Dimensional Turbulence Simulation Data using t-SNE.
201727 citationsauthor #1
A Priori Assessment of Prediction Confidence for Data-Driven Turbulence Modeling
201719 citationsauthor #1
Representation of Reynolds Stress Perturbations with Application in Machine-Learning-Assisted Turbulence Modeling
20179 citationsauthor #1
Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework
2017516 citationsauthor #1
A Physics-Informed Machine Learning Approach of Improving RANS Predicted Reynolds Stresses
201716 citationsauthor #2
Physics-Informed Machine Learning for Predictive Turbulence Modeling: A Priori Assessment of Prediction Confidence
201618 citationsauthor #1
Quantifying Model Form Uncertainty in RANS Simulation of Wing-Body Junction Flow
20163 citationsauthor #1
Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data
2016643 citationsauthor #2
A Priori Assessment of Prediction Confidence for Data-Driven Turbulence Modeling
201679 citationsauthor #1
A Bayesian Calibration–Prediction Method for Reducing Model-Form Uncertainties with Application in RANS Simulations
20160 citationsauthor #1
Physics-Informed Machine Learning for Predictive Turbulence Modeling: Towards a Complete Framework.
201628 citationsauthor #2
Reducing RANS Model Error Using Random Forest
20160 citationsauthor #2
Physics-Informed Machine Learning for Predictive Turbulence Modeling: Using Data to Improve RANS Modeled Reynolds Stresses
201630 citationsauthor #2
A Physics-Informed Machine Learning Framework for RANS-based Predictive Turbulence Modeling
20160 citationsauthor #2
Are Discrepancies in RANS Modeled Reynolds Stresses Random
20162 citationsauthor #2
Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier-Stokes simulations: A data-driven, physics-informed Bayesian approach
2015308 citationsauthor #2
Incorporating Prior Knowledge for Quantifying and Reducing Model-Form Uncertainty in RANS Simulations
201514 citationsauthor #2
Quantifying Model-Form Uncertainties in Reynolds Averaged Navier-Stokes Equations: An Open-Box, Physics-Based, Bayesian Approach
20151 citationsauthor #2
A Bayesian Calibration–Prediction Method for Reducing Model-Form Uncertainties with Application in RANS Simulations
201567 citationsauthor #1
Large Eddy simulation of Trailing Edge Acoustic Emissions of an Airfoil
20150 citationsauthor #1
MIT Open Access Articles Reynolds-averaged Navier–Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned
year unknown1 citationsauthor #1

TIMELINE

2015 390 citations

2016 803 citations

2017 587 citations

2018 450 citations

2019 408 citations

2020 1 citations

Unknown 1 citations

Jin-Long Wu

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