Bethany Lusch

2.1k total citations · 2 hit papers
25 papers, 1.3k citations indexed

About

Bethany Lusch is a scholar working on Computational Mechanics, Statistical and Nonlinear Physics and Artificial Intelligence. According to data from OpenAlex, Bethany Lusch has authored 25 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Computational Mechanics, 11 papers in Statistical and Nonlinear Physics and 4 papers in Artificial Intelligence. Recurrent topics in Bethany Lusch's work include Model Reduction and Neural Networks (11 papers), Fluid Dynamics and Turbulent Flows (8 papers) and Advanced Combustion Engine Technologies (4 papers). Bethany Lusch is often cited by papers focused on Model Reduction and Neural Networks (11 papers), Fluid Dynamics and Turbulent Flows (8 papers) and Advanced Combustion Engine Technologies (4 papers). Bethany Lusch collaborates with scholars based in United States, France and Singapore. Bethany Lusch's co-authors include J. Nathan Kutz, Steven L. Brunton, Romit Maulik, Prasanna Balaprakash, Elise Jennings, Himanshu Sharma, Arvind Mohan, Daniel Livescu, Sandeep Madireddy and Pedro D. Maia and has published in prestigious journals such as Nature Communications, Computer Methods in Applied Mechanics and Engineering and Physics of Fluids.

In The Last Decade

Bethany Lusch

24 papers receiving 1.2k citations

Hit Papers

align trajectories

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.

Deep learning for universal linear embeddings of nonlinear dynamics

2018 793 citations Bethany Lusch, J. Nathan Kutz et al. Nature Communications profile →
Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders

2021 185 citations Romit Maulik, Bethany Lusch et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Bethany Lusch United States	11	808	480	278	187	170	25	1.3k
Samuel Rudy United States	9	896 1.1×	290 0.6×	333 1.2×	227 1.2×	95 0.6×	17	1.3k
Alexey Radul United States	8	660 0.8×	286 0.6×	377 1.4×	96 0.5×	133 0.8×	17	1.4k
Eurika Kaiser United States	10	529 0.7×	355 0.7×	139 0.5×	170 0.9×	177 1.0×	25	912
Yibo Yang United States	11	646 0.8×	280 0.6×	217 0.8×	70 0.4×	145 0.9×	23	1.2k
Yinhao Zhu United States	8	591 0.7×	299 0.6×	207 0.7×	75 0.4×	109 0.6×	17	1.4k
Marko Budišić United States	7	598 0.7×	274 0.6×	111 0.4×	161 0.9×	76 0.4×	15	818
Jean-Christophe Loiseau France	15	403 0.5×	473 1.0×	113 0.4×	132 0.7×	170 1.0×	26	903
Hayden Schaeffer United States	12	584 0.7×	259 0.5×	230 0.8×	212 1.1×	41 0.2×	39	987
Soledad Le Clainche Spain	24	885 1.1×	1.2k 2.6×	72 0.3×	155 0.8×	544 3.2×	96	1.8k
Kamyar Azizzadenesheli United States	14	321 0.4×	174 0.4×	326 1.2×	122 0.7×	117 0.7×	36	1.1k

Countries citing papers authored by Bethany Lusch

Since Specialization

Citations

This map shows the geographic impact of Bethany Lusch'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 Bethany Lusch with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bethany Lusch more than expected).

Fields of papers citing papers by Bethany Lusch

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Bethany Lusch. 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 Bethany Lusch. The network helps show where Bethany Lusch may publish in the future.

Co-authorship network of co-authors of Bethany Lusch

This figure shows the co-authorship network connecting the top 25 collaborators of Bethany Lusch. A scholar is included among the top collaborators of Bethany Lusch 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 Bethany Lusch. Bethany Lusch is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Pal, Pinaki, et al.. (2025). Mesh-based super-resolution of fluid flows with multiscale graph neural networks. Computer Methods in Applied Mechanics and Engineering. 443. 118072–118072. 1 indexed citations

Lusch, Bethany, Murali Emani, Filippo Simini, et al.. (2024). A Multi-Level, Multi-Scale Visual Analytics Approach to Assessment of Multifidelity HPC Systems. 478–488.

Lusch, Bethany, et al.. (2024). Scalable and Consistent Graph Neural Networks for Distributed Mesh-based Data-driven Modeling. 1058–1070. 2 indexed citations

Rhone, Trevor David, Bethany Lusch, Marios Mattheakis, et al.. (2023). Artificial Intelligence Guided Studies of van der Waals Magnets. Advanced Theory and Simulations. 6(6). 13 indexed citations

Maulik, Romit, Jiali Wang, Gianmarco Mengaldo, et al.. (2022). Efficient high-dimensional variational data assimilation with machine-learned reduced-order models. Geoscientific model development. 15(8). 3433–3445. 21 indexed citations

Lusch, Bethany, Murali Emani, Filippo Simini, et al.. (2022). Toward an In-Depth Analysis of Multifidelity High Performance Computing Systems. 716–725. 2 indexed citations

Maulik, Romit, et al.. (2022). PythonFOAM: In-situ data analyses with OpenFOAM and Python. Journal of Computational Science. 62. 101750–101750. 12 indexed citations

Mondal, Sudeepta, et al.. (2021). Accelerating the Generation of Static Coupling Injection Maps Using a Data-Driven Emulator. SAE International Journal of Advances and Current Practices in Mobility. 3(3). 1408–1424. 10 indexed citations

Mondal, Sudeepta, et al.. (2021). Machine Learning-Enabled Prediction of Transient Injection Map In Automotive Injectors With Uncertainty Quantification. 2 indexed citations

10.

Maulik, Romit, et al.. (2020). A turbulent eddy-viscosity surrogate modeling framework for Reynolds-averaged Navier-Stokes simulations. Computers & Fluids. 227. 104777–104777. 61 indexed citations

11.

Maulik, Romit, Arvind Mohan, Bethany Lusch, et al.. (2020). Time-series learning of latent-space dynamics for reduced-order model closure. Physica D Nonlinear Phenomena. 405. 132368–132368. 78 indexed citations

12.

Torelli, Roberto, et al.. (2020). DATA-DRIVEN MODEL REDUCTION OF MULTIPHASE FLOW IN A SINGLE-HOLE AUTOMOTIVE INJECTOR. Atomization and Sprays. 30(6). 401–429. 12 indexed citations

13.

Maulik, Romit, et al.. (2019). Accelerating RANS turbulence modeling using potential flow and machine learning. arXiv (Cornell University). 9 indexed citations

14.

Maulik, Romit, et al.. (2019). Accelerating RANS simulations using a data-driven framework for eddy-viscosity emulation. arXiv (Cornell University). 2 indexed citations

15.

Lusch, Bethany, et al.. (2019). MELA: A Visual Analytics Tool for Studying Multifidelity HPC System Logs. 13–18. 11 indexed citations

16.

Lusch, Bethany, C. Eric, & J. Nathan Kutz. (2019). Shape Constrained Tensor Decompositions. 287–297. 1 indexed citations

17.

Lusch, Bethany, J. Nathan Kutz, & Steven L. Brunton. (2018). Deep learning for universal linear embeddings of nonlinear dynamics. Nature Communications. 9(1). 4950–4950. 793 indexed citations breakdown →

18.

Lusch, Bethany, et al.. (2017). Data-driven discovery of Koopman eigenfunctions using deep learning. Bulletin of the American Physical Society. 1 indexed citations

19.

Lusch, Bethany, Pedro D. Maia, & J. Nathan Kutz. (2016). Inferring connectivity in networked dynamical systems: Challenges using Granger causality. Physical review. E. 94(3). 32220–32220. 28 indexed citations

20.

Gillenwater, Jennifer, Rishabh Iyer, Bethany Lusch, Rahul Kidambi, & Jeff Bilmes. (2015). Submodular hamming metrics. arXiv (Cornell University). 28. 3141–3149. 1 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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