David Sondak

608 total citations
21 papers, 321 citations indexed

About

David Sondak is a scholar working on Computational Mechanics, Statistical and Nonlinear Physics and Computational Theory and Mathematics. According to data from OpenAlex, David Sondak has authored 21 papers receiving a total of 321 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Computational Mechanics, 10 papers in Statistical and Nonlinear Physics and 5 papers in Computational Theory and Mathematics. Recurrent topics in David Sondak's work include Fluid Dynamics and Turbulent Flows (10 papers), Model Reduction and Neural Networks (8 papers) and Advanced Numerical Methods in Computational Mathematics (4 papers). David Sondak is often cited by papers focused on Fluid Dynamics and Turbulent Flows (10 papers), Model Reduction and Neural Networks (8 papers) and Advanced Numerical Methods in Computational Mathematics (4 papers). David Sondak collaborates with scholars based in United States, Italy and United Kingdom. David Sondak's co-authors include Pavlos Protopapas, Marios Mattheakis, Leslie Smith, Devansh Agarwal, Shuheng Liu, Rui Fang, Fabian Waleffe, Sauro Succi, Assad A. Oberai and Stephen Roberts and has published in prestigious journals such as The Astrophysical Journal, Journal of Fluid Mechanics and Journal of Computational Physics.

In The Last Decade

David Sondak

20 papers receiving 312 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
David Sondak United States	9	186	150	61	31	31	21	321
Jared Callaham United States	9	239 1.3×	118 0.8×	85 1.4×	50 1.6×	18 0.6×	13	404
Yiping Lu China	5	260 1.4×	93 0.6×	101 1.7×	38 1.2×	29 0.9×	17	386
George Em Karniadakis United States	6	219 1.2×	126 0.8×	63 1.0×	47 1.5×	44 1.4×	14	369
Tong Qin United States	7	203 1.1×	165 1.1×	68 1.1×	29 0.9×	18 0.6×	11	387
Mario De Florio United States	12	273 1.5×	91 0.6×	103 1.7×	81 2.6×	29 0.9×	19	460
Ignacio Tomaš United States	8	105 0.6×	280 1.9×	24 0.4×	34 1.1×	38 1.2×	16	443
Pengzhan Jin China	7	200 1.1×	51 0.3×	102 1.7×	18 0.6×	22 0.7×	10	291
Peter J. Baddoo United Kingdom	9	133 0.7×	165 1.1×	20 0.3×	108 3.5×	15 0.5×	26	303
Kathleen Champion United States	3	221 1.2×	52 0.3×	82 1.3×	31 1.0×	13 0.4×	4	339

Countries citing papers authored by David Sondak

Since Specialization

Citations

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

Fields of papers citing papers by David Sondak

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Sondak

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

All Works

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20 of 20 papers shown

Burkhart, Blakesley, et al.. (2024). Extending a Physics-informed Machine-learning Network for Superresolution Studies of Rayleigh–Bénard Convection. The Astrophysical Journal. 964(1). 2–2. 7 indexed citations

Mattheakis, Marios, et al.. (2022). Hamiltonian neural networks for solving equations of motion. Physical review. E. 105(6). 65305–65305. 54 indexed citations

Sondak, David, et al.. (2021). Convolutional Neural Network Models and Interpretability for the Anisotropic Reynolds Stress Tensor in Turbulent One-dimensional Flows. arXiv (Cornell University). 8 indexed citations

Sondak, David & Pavlos Protopapas. (2021). Learning a reduced basis of dynamical systems using an autoencoder. Physical review. E. 104(3). 34202–34202. 6 indexed citations

Mattheakis, Marios, et al.. (2021). Port-Hamiltonian neural networks for learning explicit time-dependent dynamical systems. Physical review. E. 104(3). 34312–34312. 24 indexed citations

Sondak, David, et al.. (2020). Finding multiple solutions of odes with neural networks. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 2587. 1–7. 4 indexed citations

Sondak, David, et al.. (2020). NeuroDiffEq: A Python package for solving differential equations with neural networks. Zenodo (CERN European Organization for Nuclear Research). 4 indexed citations

Sondak, David, et al.. (2020). NeuroDiffEq: A Python package for solving differential equations with neural networks. The Journal of Open Source Software. 5(46). 1931–1931. 79 indexed citations

Fang, Rui, David Sondak, Pavlos Protopapas, & Sauro Succi. (2019). Neural network models for the anisotropic Reynolds stress tensor in turbulent channel flow. Journal of Turbulence. 21(9-10). 525–543. 40 indexed citations

10.

Sondak, David, Todd Oliver, Chris Simmons, & Robert Moser. (2017). An Inadequacy Formulation for an Uncertain Flamelet Model. 1 indexed citations

11.

Sondak, David, et al.. (2016). Can phoretic particles swim in two dimensions?. Physical review. E. 94(6). 62606–62606. 13 indexed citations

12.

Sondak, David, Leslie Smith, & Fabian Waleffe. (2015). Optimal heat transport solutions for Rayleigh–Bénard convection. Journal of Fluid Mechanics. 784. 565–595. 30 indexed citations

13.

Sondak, David, John N. Shadid, Assad A. Oberai, et al.. (2015). A new class of finite element variational multiscale turbulence models for incompressible magnetohydrodynamics. Journal of Computational Physics. 295. 596–616. 13 indexed citations

14.

Oberai, Assad A., et al.. (2014). A residual based eddy viscosity model for the large eddy simulation of turbulent flows. Computer Methods in Applied Mechanics and Engineering. 282. 54–70. 12 indexed citations

15.

Sondak, David. (2013). Novel residual-based large eddy simulation turbulence models for incompressible magnetohydrodynamics. 1 indexed citations

16.

Sondak, David & Assad A. Oberai. (2012). LES models for incompressible magnetohydrodynamics derived from the variational multiscale formulation. Bulletin of the American Physical Society. 54. 1 indexed citations

17.

Sondak, David & Assad A. Oberai. (2012). Large eddy simulation models for incompressible magnetohydrodynamics derived from the variational multiscale formulation. Physics of Plasmas. 19(10). 4 indexed citations

18.

Sondak, David & Assad A. Oberai. (2010). Towards a stabilized finite element method for the MHD equations. Bulletin of the American Physical Society. 52. 2 indexed citations

19.

Oberai, Assad A. & David Sondak. (2010). Application of the variational Germano identity to the variational multiscale formulation. International Journal for Numerical Methods in Biomedical Engineering. 27(2). 335–344. 3 indexed citations

20.

Schuster, Eugenio, et al.. (2009). Optimal tuning of tokamak plasma equilibrium controllers in the presence of time delays. 1188–1194. 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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