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Jack Weatheritt
Jack Weatheritt
Tractable AI
Verified email at tractable.ai
Title
Cited by
Cited by
Year
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
2712016
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
1862020
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
1522017
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
602018
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
582017
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
442018
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
412020
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
412019
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
322017
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
242018
Hybrid Reynolds-averaged/large-eddy simulation methodology from symbolic regression: formulation and application
J Weatheritt, RD Sandberg
AIAA Journal 55 (11), 3734-3746, 2017
242017
Improved junction body flow modeling through data-driven symbolic regression
J Weatheritt, RD Sandberg
Journal of Ship Research 63 (04), 283-293, 2019
132019
The development of data driven approaches to further turbulence closures
J Weatheritt
University of Southampton, 2015
122015
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
72015
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
62016
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
62016
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
52020
Hybrid simulation of the surface mounted square cylinder
J Weatheritt, RD Sandberg
Proceedings, 5-8, 2016
32016
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
22020
GaLeNet: Multimodal Learning for Disaster Prediction, Management and Relief
R Saha, M Fang, A Yasodhara, K Truskovskyi, A Asgarian, D Homola, ...
arXiv preprint arXiv:2206.09242, 2022
12022
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