A novel evolutionary algorithm applied to algebraic modifications of the RANS stress–strain relationship J Weatheritt, R Sandberg Journal of Computational Physics 325, 22-37, 2016 | 321 | 2016 |
RANS turbulence model development using CFD-driven machine learning Y Zhao, HD Akolekar, J Weatheritt, V Michelassi, RD Sandberg Journal of Computational Physics 411, 109413, 2020 | 240 | 2020 |
The development of algebraic stress models using a novel evolutionary algorithm J Weatheritt, RD Sandberg International Journal of Heat and Fluid Flow 68, 298-318, 2017 | 168 | 2017 |
Applying machine learnt explicit algebraic stress and scalar flux models to a fundamental trailing edge slot RD Sandberg, R Tan, J Weatheritt, A Ooi, A Haghiri, V Michelassi, ... Journal of Turbomachinery 140 (10), 101008, 2018 | 68 | 2018 |
Machine learning for turbulence model development using a high-fidelity HPT cascade simulation J Weatheritt, R Pichler, RD Sandberg, G Laskowski, V Michelassi Turbo Expo: Power for Land, Sea, and Air 50794, V02BT41A015, 2017 | 68 | 2017 |
Development and use of machine-learnt algebraic Reynolds stress models for enhanced prediction of wake mixing in low-pressure turbines HD Akolekar, J Weatheritt, N Hutchins, RD Sandberg, G Laskowski, ... Journal of Turbomachinery 141 (4), 041010, 2019 | 55 | 2019 |
Application of an evolutionary algorithm to LES modelling of turbulent transport in premixed flames M Schoepplein, J Weatheritt, R Sandberg, M Talei, M Klein Journal of Computational Physics 374, 1166-1179, 2018 | 52 | 2018 |
Data-driven scalar-flux model development with application to jet in cross flow J Weatheritt, Y Zhao, RD Sandberg, S Mizukami, K Tanimoto International Journal of Heat and Mass Transfer 147, 118931, 2020 | 50 | 2020 |
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 | 39 | 2017 |
Development and use of machine-learnt algebraic Reynolds stress models for enhanced prediction of wake mixing in LPTs HD Akolekar, J Weatheritt, N Hutchins, RD Sandberg, G Laskowski, ... Turbo Expo: Power for Land, Sea, and Air 51012, V02CT42A009, 2018 | 28 | 2018 |
Hybrid Reynolds-averaged/large-eddy simulation methodology from symbolic regression: formulation and application J Weatheritt, RD Sandberg AIAA Journal 55 (11), 3734-3746, 2017 | 25 | 2017 |
Improved junction body flow modeling through data-driven symbolic regression J Weatheritt, RD Sandberg Journal of Ship Research 63 (04), 283-293, 2019 | 14 | 2019 |
The development of data driven approaches to further turbulence closures J Weatheritt University of Southampton, 2015 | 12 | 2015 |
Use of Symbolic Regression for construction of Reynolds-stress damping functions for Hybrid RANS/LES J Weatheritt, RD Sandberg 53rd AIAA Aerospace Sciences Meeting, 0312, 2015 | 7 | 2015 |
Transfer learning for brain segmentation: Pre-task selection and data limitations J Weatheritt, D Rueckert, R Wolz Medical Image Understanding and Analysis: 24th Annual Conference, MIUA 2020 …, 2020 | 6 | 2020 |
Reynolds stress structures in the hybrid RANS/LES of a planar channel J Weatheritt, R Sandberg, A Lozano-Durán Journal of Physics: Conference Series 708 (1), 012008, 2016 | 6 | 2016 |
A new Reynolds stress damping function for hybrid RANS/LES with an evolved functional form J Weatheritt, RD Sandberg Advances in Computation, Modeling and Control of Transitional and Turbulent …, 2016 | 6 | 2016 |
Hybrid simulation of the surface mounted square cylinder J Weatheritt, RD Sandberg Proceedings, 5-8, 2016 | 3 | 2016 |
Alzheimer's disease detection using explainable AI on PET images J Weatheritt, A Palombit, R Manber, R Wolz Alzheimer's & Dementia 17, e053831, 2021 | 2 | 2021 |
Application of an Evolutionary Algorithm to LES Modelling of Turbulent Premixed Flames M Schöpplein, J Weatheritt, M Talei, M Klein, RD Sandberg Data Analysis for Direct Numerical Simulations of Turbulent Combustion: From …, 2020 | 2 | 2020 |