Jack Weatheritt

1.2k total citations · 1 hit paper
17 papers, 844 citations indexed

About

Jack Weatheritt is a scholar working on Computational Mechanics, Aerospace Engineering and Statistical and Nonlinear Physics. According to data from OpenAlex, Jack Weatheritt has authored 17 papers receiving a total of 844 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Computational Mechanics, 7 papers in Aerospace Engineering and 6 papers in Statistical and Nonlinear Physics. Recurrent topics in Jack Weatheritt's work include Fluid Dynamics and Turbulent Flows (13 papers), Model Reduction and Neural Networks (6 papers) and Heat Transfer Mechanisms (6 papers). Jack Weatheritt is often cited by papers focused on Fluid Dynamics and Turbulent Flows (13 papers), Model Reduction and Neural Networks (6 papers) and Heat Transfer Mechanisms (6 papers). Jack Weatheritt collaborates with scholars based in Australia, United States and United Kingdom. Jack Weatheritt's co-authors include Richard D. Sandberg, Vittorio Michelassi, Harshal D. Akolekar, Yaomin Zhao, Gregory M. Laskowski, Nicholas Hutchins, Andrew Ooi, Mohsen Talei, Richard Pichler and Markus Klein and has published in prestigious journals such as Journal of Computational Physics, International Journal of Heat and Mass Transfer and AIAA Journal.

In The Last Decade

Jack Weatheritt

17 papers receiving 800 citations

Hit Papers

RANS turbulence model development using CFD-driven machin... 2020 2026 2022 2024 2020 50 100 150
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. RANS turbulence model development using CFD-driven machine learning
    2020 199 citations Yaomin Zhao, Harshal D. Akolekar et al. Journal of Computational Physics profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Jack Weatheritt Australia 12 692 461 323 187 131 17 844
Yilang Liu China 14 733 1.1× 425 0.9× 326 1.0× 87 0.5× 128 1.0× 30 870
Maziar S. Hemati United States 11 606 0.9× 461 1.0× 343 1.1× 80 0.4× 57 0.4× 52 874
Yaomin Zhao China 18 694 1.0× 241 0.5× 345 1.1× 216 1.2× 122 0.9× 51 844
Gahl Berkooz Sweden 2 365 0.5× 324 0.7× 130 0.4× 62 0.3× 61 0.5× 2 606
Paul G. A. Cizmas United States 17 554 0.8× 215 0.5× 409 1.3× 129 0.7× 66 0.5× 86 824
Onofrio Semeraro France 14 582 0.8× 201 0.4× 334 1.0× 48 0.3× 143 1.1× 29 657
Linyang Zhu China 6 353 0.5× 277 0.6× 191 0.6× 56 0.3× 55 0.4× 11 452
Xianxu Yuan China 17 914 1.3× 161 0.3× 423 1.3× 83 0.4× 114 0.9× 114 1.0k
Christophe Corre France 18 705 1.0× 75 0.2× 243 0.8× 70 0.4× 109 0.8× 52 912
Hamidreza Eivazi Sweden 10 302 0.4× 340 0.7× 163 0.5× 81 0.4× 39 0.3× 18 547

Countries citing papers authored by Jack Weatheritt

Since Specialization
Citations

This map shows the geographic impact of Jack Weatheritt's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jack Weatheritt with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jack Weatheritt more than expected).

Fields of papers citing papers by Jack Weatheritt

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jack Weatheritt. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jack Weatheritt. The network helps show where Jack Weatheritt may publish in the future.

Co-authorship network of co-authors of Jack Weatheritt

This figure shows the co-authorship network connecting the top 25 collaborators of Jack Weatheritt. A scholar is included among the top collaborators of Jack Weatheritt based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jack Weatheritt. Jack Weatheritt is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

17 of 17 papers shown
1.
Peraza, Luis R., Jack Weatheritt, & Robin Wolz. (2023). A machine learning‐based quality control tool for functional MRI images. Alzheimer s & Dementia. 19(S3). 1 indexed citations
2.
Weatheritt, Jack, et al.. (2021). Alzheimer's disease detection using explainable AI on PET images. Alzheimer s & Dementia. 17(S4). 1 indexed citations
3.
Zhao, Yaomin, Harshal D. Akolekar, Jack Weatheritt, Vittorio Michelassi, & Richard D. Sandberg. (2020). RANS turbulence model development using CFD-driven machine learning. Journal of Computational Physics. 411. 109413–109413. 199 indexed citations breakdown →
4.
Weatheritt, Jack & Richard D. Sandberg. (2019). Improved Junction Body Flow Modeling Through Data-Driven Symbolic Regression. Journal of Ship Research. 63(4). 283–293. 9 indexed citations
5.
Weatheritt, Jack, et al.. (2019). Data-driven scalar-flux model development with application to jet in cross flow. International Journal of Heat and Mass Transfer. 147. 118931–118931. 37 indexed citations
6.
Akolekar, Harshal D., Jack Weatheritt, Nicholas Hutchins, et al.. (2018). Development and Use of Machine-Learnt Algebraic Reynolds Stress Models for Enhanced Prediction of Wake Mixing in Low-Pressure Turbines. Journal of Turbomachinery. 141(4). 41 indexed citations
7.
Sandberg, Richard D., et al.. (2018). Applying Machine Learnt Explicit Algebraic Stress and Scalar Flux Models to a Fundamental Trailing Edge Slot. Journal of Turbomachinery. 140(10). 38 indexed citations
8.
Sandberg, Richard D., et al.. (2018). Applying Machine Learnt Explicit Algebraic Stress and Scalar Flux Models to a Fundamental Trailing Edge Slot. Minerva Access (University of Melbourne). 22 indexed citations
9.
Akolekar, Harshal D., Jack Weatheritt, Nicholas Hutchins, et al.. (2018). Development and Use of Machine-Learnt Algebraic Reynolds Stress Models for Enhanced Prediction of Wake Mixing in LPTs. Minerva Access (University of Melbourne). 14 indexed citations
10.
Weatheritt, Jack, et al.. (2018). Application of an evolutionary algorithm to LES modelling of turbulent transport in premixed flames. Journal of Computational Physics. 374. 1166–1179. 41 indexed citations
11.
Weatheritt, Jack, et al.. (2017). A Comparative Study of Contrasting Machine Learning Frameworks Applied to RANS Modeling of Jets in Crossflow. Open Archive Toulouse Archive Ouverte (University of Toulouse). 25 indexed citations
12.
Weatheritt, Jack & Richard D. Sandberg. (2017). The development of algebraic stress models using a novel evolutionary algorithm. International Journal of Heat and Fluid Flow. 68. 298–318. 116 indexed citations
13.
Weatheritt, Jack, Richard Pichler, Richard D. Sandberg, Gregory M. Laskowski, & Vittorio Michelassi. (2017). Machine Learning for Turbulence Model Development Using a High-Fidelity HPT Cascade Simulation. Minerva Access (University of Melbourne). 38 indexed citations
14.
Weatheritt, Jack & Richard D. Sandberg. (2017). Hybrid Reynolds-Averaged/Large-Eddy Simulation Methodology from Symbolic Regression: Formulation and Application. AIAA Journal. 55(11). 3734–3746. 17 indexed citations
15.
Weatheritt, Jack & Richard D. Sandberg. (2016). A novel evolutionary algorithm applied to algebraic modifications of the RANS stress–strain relationship. Journal of Computational Physics. 325. 22–37. 240 indexed citations
16.
Weatheritt, Jack, Richard D. Sandberg, & Adrián Lozano-Durán. (2016). Reynolds Stress Structures in the Hybrid RANS/LES of a Planar Channel. Journal of Physics Conference Series. 708. 12008–12008. 1 indexed citations
17.
Weatheritt, Jack & Richard D. Sandberg. (2015). Use of Symbolic Regression for construction of Reynolds-stress damping functions for Hybrid RANS/LES. 53rd AIAA Aerospace Sciences Meeting. 4 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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