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Publication	Type	Year
Near-Wall Turbulence Modeling using Neural Networks Sandia Machine Learning and Deep Learning Workshop Matthew F. Barone, Nathan Miller, Warren Leon Davis, Jeffrey A. Fike https://www.osti.gov/search/identifier:1641518	Conference Paper – 2019 Conference Paper	2019
Machine-Learned Models for Near-Wall Turbulence DOE ASC V&V Lead Visit to Sandia Matthew F. Barone, Nathan Miller, Warren Leon Davis, Jeffrey A. Fike https://www.osti.gov/search/identifier:1645355	Presentation (non-conference) – 2019 Presentation (non-conference)	2019
Near-Wall Modeling using Coordinate Frame Invariant Representations and Neural Networks AIAA Aviation Forum Nathan Miller, Matthew F. Barone, Warren Leon Davis, Jeffrey A. Fike https://www.osti.gov/search/identifier:1640064	Conference Paper – 2019 Conference Paper	2019
Coordinate-Invariant Near-Wall Turbulence Modeling via Neural Networks Machine Learning for Computational Fluid and Solid Dynamics Nathan Miller, Matthew F. Barone, Warren Leon Davis, Jeffrey A. Fike https://www.osti.gov/search/identifier:1601537	Conference Paper – 2019 Conference Paper	2019
Coordinate-Invariant Near-Wall Turbulence Modeling via Neural Networks Machine Learning for Computational Fluid and Solid Dynamics Nathan Miller, Matthew F. Barone, Warren Leon Davis, Jeffrey A. Fike	Abstract – 2019 Abstract	2019
Wall Shear Stress Prediction using Coordinate Frame Invariant Representations and Neural Networks AIAA Aviation and Aeronautics Forum and Exposition Nathan Miller, Matthew F. Barone, Warren Leon Davis, Jeffrey A. Fike	Abstract – 2018 Abstract	2018
Deploy production sliding mesh capability with linear solver benchmarking Stefan P. Domino, Matthew F. Barone, Alan B. Williams, Robert C. Knaus, James R. Overfelt https://www.osti.gov/search/identifier:1527310	Report – 2017 Report	2017
Turbulent Flow UQ Using Machine Learning Techniques 2017 Sandia Machine Learning & Deep Learning Conference Warren Leon Davis, Matthew F. Barone https://www.osti.gov/search/identifier:1464899	Conference Paper – 2017 Conference Paper	2017
Machine Learning Models of Errors in LES Predictions of Surface Pressure Fluctuations AIAA Aviation 2017 Matthew F. Barone, Jeffrey A. Fike, Kamaljit Singh Chowdhary, Warren Leon Davis, Julia Ling, Shawn Martin https://www.osti.gov/search/identifier:1458163	Conference Paper – 2017 Conference Paper	2017
Machine Learning Models of Errors in Large Eddy Simulation Predictions of Surface Pressure Fluctuations AIAA Aviation Conference Matthew F. Barone, Jeffrey A. Fike, Kamaljit Singh Chowdhary, Warren Leon Davis, Julia Ling, Shawn Martin https://www.osti.gov/search/identifier:1367203	Conference Paper – 2017 Conference Paper	2017
Convolutional Neural Networks for Frequency Response Predictions ASME V&V Conference Andrew Wilson, Daniel Wade, Julia Ling, Kamaljit Singh Chowdhary, Warren Leon Davis, Matthew F. Barone, Matthew F. Barone, Jeffrey A. Fike https://www.osti.gov/search/identifier:1457870	Conference Paper – 2017 Conference Paper	2017
Investigating Turbulent Wall Pressure Fluctuations using Machine Learning Techniques Visit to MIT Matthew F. Barone, Julia Ling, Warren Leon Davis, Kamaljit Singh Chowdhary, Jeffrey A. Fike https://www.osti.gov/search/identifier:1456663	Presentation (non-conference) – 2017 Presentation (non-conference)	2017
Approaches for building stable projection-based reduced order models for compressible flow Virginia Tech Seminar Irina Kalashnikova Tezaur, Matthew F. Barone, Jeffrey A. Fike, Maciej (UIUC) Balajewicz, Srinivasan Arunajatesan, Bart G van Bloemen Waanders, Earl (Duke) Dowell https://www.osti.gov/search/identifier:1456589	Presentation (non-conference) – 2017 Presentation (non-conference)	2017
Convolutional Neural Networks for Frequency Response Predictions ASME V&V Andrew Wilson, Daniel Wade, Julia Ling, Kamaljit Singh Chowdhary, Warren Leon Davis, Matthew F. Barone, Jeffrey A. Fike	Abstract – 2017 Abstract	2017
Development of Machine Learning Models for Turbulent Wall Pressure Fluctuations AIAA SciTech Julia Ling, Matthew F. Barone, Warren Leon Davis, Kamaljit Singh Chowdhary, Jeffrey A. Fike https://www.osti.gov/search/identifier:1416699	Conference Paper – 2017 Conference Paper	2017
Development of Machine Learning Models for Turbulent Wall Pressure Fluctuations AIAA SciTech Julia Ling, Matthew F. Barone, Warren Leon Davis, Kamaljit Singh Chowdhary, Jeffrey A. Fike https://www.osti.gov/search/identifier:1422115	Conference Paper – 2016 Conference Paper	2016
Development of Machine Learning Models for Turbulent Wall Pressure Fluctuations AIAA SciTech 2017 Matthew F. Barone, Julia Ling, Warren Leon Davis https://www.osti.gov/search/identifier:1368710	Conference Paper – 2016 Conference Paper	2016
Stabilization of Projection-Based Reduced Order Models via Optimization-Based Eigenvalue Reassignment 1st Pan American Congress on Computational Mechanics (PANACM) 2015 Irina Kalashnikova Tezaur, Bart G van Bloemen Waanders, Srinivasan Arunajatesan, Matthew F. Barone, Jeffrey A. Fike https://www.osti.gov/search/identifier:1248705	Conference Paper – 2015 Conference Paper	2015
Multifidelity Uncertainty Quantification Using Spectral Stochastic Discrepancy Models Handbook of Uncertainty Quantification Michael S. Eldred, Leo W.T. Ng, Matthew F. Barone, Stefan P. Domino https://www.osti.gov/search/identifier:1191876	Book – 2015 Book	2015
Stabilization of Projection-Based Reduced Order Models via Optimization-Based Eigenvalue Reassignment 1st Pan American Congress on Computational Mechanics (PANACM) 2015 Irina Kalashnikova Tezaur, Bart G van Bloemen Waanders, Srinivasan Arunajatesan, Matthew F. Barone, Jeffrey A. Fike	Abstract – 2014 Abstract	2014
Energy-Stable Galerkin Reduced Order Models for Nonlinear Compressible Flow World Congress on Computational Mechanics (WCCM) XI Irina Kalashnikova Tezaur, Matthew F. Barone, Jeffrey A. Fike, Srinivasan Arunajatesan, Bart G van Bloemen Waanders https://www.osti.gov/search/identifier:1496555	Presentation (non-conference) – 2014 Presentation (non-conference)	2014
Multi-Fidelity Uncertainty Quantification: Application to a Vertical Axis Wind Turbine Under an Extreme Gust AIAA Aviation 2014 Matthew F. Barone, Michael S. Eldred, A. Santiago Padron, Juan J. Alonso, F. Palacios https://www.osti.gov/search/identifier:1315382	Conference Paper – 2014 Conference Paper	2014
Reduced Order Modeling for Prediction and Control of Large-Scale Systems Irina Kalashnikova Tezaur, Srinivasan Arunajatesan, Matthew F. Barone, Bart G van Bloemen Waanders, Jeffrey A. Fike https://www.osti.gov/search/identifier:1177206	SAND Report – 2014 SAND Report	2014
Stabilized Projection-Based Reduced Order Models for Uncertainty Quantification SIAM Conference on Uncertainty Quantification (UQ14) Irina Kalashnikova Tezaur, Bart G van Bloemen Waanders, Srinivasan Arunajatesan, Matthew F. Barone https://www.osti.gov/search/identifier:1315692	Conference Paper – 2014 Conference Paper	2014
Stabilization of Projection-Based Reduced Order Models via Eigenvalue Reassignment Computer Methods in Applied Mechanics and Engineering Irina Kalashnikova Tezaur, Bart G van Bloemen Waanders, Srinivasan Arunajatesan, Matthew F. Barone https://www.osti.gov/search/identifier:1095826	Journal Article – 2013 Journal Article	2013
Document Title	Type	Year

Results 1–25 of 31

Publications

Near-Wall Turbulence Modeling using Neural Networks

Machine-Learned Models for Near-Wall Turbulence

Near-Wall Modeling using Coordinate Frame Invariant Representations and Neural Networks

Coordinate-Invariant Near-Wall Turbulence Modeling via Neural Networks

Coordinate-Invariant Near-Wall Turbulence Modeling via Neural Networks

Wall Shear Stress Prediction using Coordinate Frame Invariant Representations and Neural Networks

Deploy production sliding mesh capability with linear solver benchmarking

Turbulent Flow UQ Using Machine Learning Techniques

Machine Learning Models of Errors in LES Predictions of Surface Pressure Fluctuations

Machine Learning Models of Errors in Large Eddy Simulation Predictions of Surface Pressure Fluctuations

Convolutional Neural Networks for Frequency Response Predictions

Investigating Turbulent Wall Pressure Fluctuations using Machine Learning Techniques

Approaches for building stable projection-based reduced order models for compressible flow

Convolutional Neural Networks for Frequency Response Predictions

Development of Machine Learning Models for Turbulent Wall Pressure Fluctuations

Development of Machine Learning Models for Turbulent Wall Pressure Fluctuations

Development of Machine Learning Models for Turbulent Wall Pressure Fluctuations

Stabilization of Projection-Based Reduced Order Models via Optimization-Based Eigenvalue Reassignment

Multifidelity Uncertainty Quantification Using Spectral Stochastic Discrepancy Models

Stabilization of Projection-Based Reduced Order Models via Optimization-Based Eigenvalue Reassignment

Energy-Stable Galerkin Reduced Order Models for Nonlinear Compressible Flow

Multi-Fidelity Uncertainty Quantification: Application to a Vertical Axis Wind Turbine Under an Extreme Gust

Reduced Order Modeling for Prediction and Control of Large-Scale Systems

Stabilized Projection-Based Reduced Order Models for Uncertainty Quantification

Stabilization of Projection-Based Reduced Order Models via Eigenvalue Reassignment