| Reynolds averaged turbulence modelling using deep neural networks with embedded invariance J Ling, A Kurzawski, J Templeton Journal of Fluid Mechanics 807, 155-166, 2016 | 1722 | 2016 |
| Machine learning strategies for systems with invariance properties J Ling, R Jones, J Templeton Journal of Computational Physics 318, 22-35, 2016 | 439 | 2016 |
| Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty J Ling, J Templeton Physics of Fluids 27 (8), 2015 | 434 | 2015 |
| Air ionization at rock surfaces and pre-earthquake signals FT Freund, IG Kulahci, G Cyr, J Ling, M Winnick, J Tregloan-Reed, ... Journal of Atmospheric and Solar-Terrestrial Physics 71 (17-18), 1824-1834, 2009 | 262 | 2009 |
| Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery B Meredig, E Antono, C Church, M Hutchinson, J Ling, S Paradiso, ... Molecular Systems Design & Engineering 3 (5), 819-825, 2018 | 259 | 2018 |
| High-dimensional materials and process optimization using data-driven experimental design with well-calibrated uncertainty estimates J Ling, M Hutchinson, E Antono, S Paradiso, B Meredig Integrating Materials and Manufacturing Innovation 6, 207-217, 2017 | 217 | 2017 |
| Overcoming data scarcity with transfer learning ML Hutchinson, E Antono, BM Gibbons, S Paradiso, J Ling, B Meredig arXiv preprint arXiv:1711.05099, 2017 | 127 | 2017 |
| A priori assessment of prediction confidence for data-driven turbulence modeling JL Wu, JX Wang, H Xiao, J Ling Flow, Turbulence and Combustion 99, 25-46, 2017 | 112* | 2017 |
| Turbulent transport in an inclined jet in crossflow F Coletti, MJ Benson, J Ling, CJ Elkins, JK Eaton International Journal of Heat and Fluid Flow 43, 149-160, 2013 | 101 | 2013 |
| A comprehensive physics-informed machine learning framework for predictive turbulence modeling JX Wang, J Wu, J Ling, G Iaccarino, H Xiao arXiv preprint arXiv:1701.07102, 2017 | 99 | 2017 |
| Building data-driven models with microstructural images: Generalization and interpretability J Ling, M Hutchinson, E Antono, B DeCost, EA Holm, B Meredig Materials Discovery 10, 19-28, 2017 | 93 | 2017 |
| A machine learning approach for determining the turbulent diffusivity in film cooling flows PM Milani, J Ling, G Saez-Mischlich, J Bodart, JK Eaton Journal of Turbomachinery 140 (2), 021006, 2018 | 89 | 2018 |
| Uncertainty analysis and data-driven model advances for a jet-in-crossflow J Ling, A Ruiz, G Lacaze, J Oefelein Journal of Turbomachinery 139 (2), 021008, 2017 | 84 | 2017 |
| Turbulent scalar flux in inclined jets in crossflow: counter gradient transport and deep learning modelling PM Milani, J Ling, JK Eaton Journal of Fluid Mechanics 906, A27, 2021 | 68 | 2021 |
| Analysis of turbulent scalar flux models for a discrete hole film cooling flow J Ling, KJ Ryan, J Bodart, JK Eaton Journal of Turbomachinery 138 (1), 011006, 2016 | 68 | 2016 |
| Assessing the frontier: Active learning, model accuracy, and multi-objective candidate discovery and optimization Z Del Rosario, M Rupp, Y Kim, E Antono, J Ling The Journal of Chemical Physics 153 (2), 2020 | 66 | 2020 |
| Optimal turbulent schmidt number for RANS modeling of trailing edge slot film cooling J Ling, CJ Elkins, JK Eaton Journal of Engineering for Gas Turbines and Power 137 (7), 072605, 2015 | 64* | 2015 |
| Effects of varying Reynolds number, blowing ratio, and internal geometry on trailing edge cutback film cooling MJ Benson, CJ Elkins, SD Yapa, JB Ling, JK Eaton Experiments in fluids 52, 1415-1430, 2012 | 43 | 2012 |
| Thrust measurements in a low-magnetic field high-density mode in the helicon double layer thruster J Ling, MD West, T Lafleur, C Charles, RW Boswell Journal of Physics D: Applied Physics 43 (30), 305203, 2010 | 43 | 2010 |
| A comparative study of contrasting machine learning frameworks applied to RANS modeling of jets in crossflow J Weatheritt, RD Sandberg, J Ling, G Saez, J Bodart Turbo Expo: Power for Land, Sea, and Air 50794, V02BT41A012, 2017 | 42 | 2017 |
Erin AntonoCitrine InformaticsE-mailová adresa ověřena na: citrine.io
