Eric Parish

1.0k total citations · 1 hit paper
27 papers, 648 citations indexed

About

Eric Parish is a scholar working on Statistical and Nonlinear Physics, Computational Mechanics and Statistics, Probability and Uncertainty. According to data from OpenAlex, Eric Parish has authored 27 papers receiving a total of 648 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Statistical and Nonlinear Physics, 17 papers in Computational Mechanics and 9 papers in Statistics, Probability and Uncertainty. Recurrent topics in Eric Parish's work include Model Reduction and Neural Networks (20 papers), Fluid Dynamics and Turbulent Flows (10 papers) and Computational Fluid Dynamics and Aerodynamics (10 papers). Eric Parish is often cited by papers focused on Model Reduction and Neural Networks (20 papers), Fluid Dynamics and Turbulent Flows (10 papers) and Computational Fluid Dynamics and Aerodynamics (10 papers). Eric Parish collaborates with scholars based in United States, Canada and India. Eric Parish's co-authors include Karthik Duraisamy, Kookjin Lee, Kevin Carlberg, Karthikeyan Duraisamy, Francesco Rizzi, Patrick Blonigan, Praveen Chandrashekar, Francesco Rizzi, Traian Iliescu and Victor Brunini and has published in prestigious journals such as Journal of Computational Physics, Computer Methods in Applied Mechanics and Engineering and AIAA Journal.

In The Last Decade

Eric Parish

27 papers receiving 613 citations

Hit Papers

A paradigm for data-driven predictive modeling using fiel... 2015 2026 2018 2022 2015 100 200 300 400
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. A paradigm for data-driven predictive modeling using field inversion and machine learning
    2015 419 citations Eric Parish, Karthik Duraisamy 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
Eric Parish United States 9 419 404 151 113 69 27 648
Deep Ray United States 11 370 0.9× 448 1.1× 110 0.7× 122 1.1× 26 0.4× 24 727
Gahl Berkooz Sweden 2 365 0.9× 324 0.8× 130 0.9× 118 1.0× 61 0.9× 2 606
Andrea Mola Italy 12 287 0.7× 260 0.6× 86 0.6× 97 0.9× 37 0.5× 36 471
A.G. Buchan United Kingdom 12 393 0.9× 441 1.1× 216 1.4× 131 1.2× 73 1.1× 24 704
Konstantin Afanasiev Germany 5 689 1.6× 518 1.3× 151 1.0× 123 1.1× 104 1.5× 6 825
Jian Yu China 13 409 1.0× 170 0.4× 147 1.0× 70 0.6× 32 0.5× 54 573
Nicholas Geneva United States 7 233 0.6× 236 0.6× 70 0.5× 40 0.4× 37 0.5× 7 469
Yilang Liu China 14 733 1.7× 425 1.1× 326 2.2× 43 0.4× 128 1.9× 30 870
Laurent Cordier France 19 1.1k 2.5× 717 1.8× 492 3.3× 185 1.6× 202 2.9× 62 1.3k
Marek Morzyński Poland 11 989 2.4× 821 2.0× 258 1.7× 173 1.5× 141 2.0× 27 1.2k

Countries citing papers authored by Eric Parish

Since Specialization
Citations

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

Fields of papers citing papers by Eric Parish

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Eric Parish

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

All Works

20 of 20 papers shown
1.
Parish, Eric, et al.. (2025). Recent developments and research needs in turbulence modeling of hypersonic flows. Physics of Fluids. 37(3). 9 indexed citations
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Parish, Eric, et al.. (2024). Data-Driven Turbulent Prandtl Number Modeling for Hypersonic Shock–Boundary-Layer Interactions. AIAA Journal. 63(5). 1671–1692. 2 indexed citations
6.
Blonigan, Patrick & Eric Parish. (2023). Evaluation of dual-weighted residual and machine learning error estimation for projection-based reduced-order models of steady partial differential equations. Computer Methods in Applied Mechanics and Engineering. 409. 115988–115988. 4 indexed citations
7.
Parish, Eric & Francesco Rizzi. (2023). On the impact of dimensionally-consistent and physics-based inner products for POD-Galerkin and least-squares model reduction of compressible flows. Journal of Computational Physics. 491. 112387–112387. 10 indexed citations
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Blonigan, Patrick, et al.. (2022). Uncertainty Propagation of the Negative Spalart-Allmaras Turbulence Model Coefficients using Projection-based Reduced-Order Models.. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1 indexed citations
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Parish, Eric, et al.. (2021). Windowed space–time least-squares Petrov–Galerkin model order reduction for nonlinear dynamical systems. Computer Methods in Applied Mechanics and Engineering. 386. 114050–114050. 11 indexed citations
12.
Lee, Kookjin & Eric Parish. (2021). Parameterized neural ordinary differential equations: applications to computational physics problems. Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences. 477(2253). 20210162–20210162. 32 indexed citations
13.
Rizzi, Francesco, et al.. (2021). A compute-bound formulation of Galerkin model reduction for linear time-invariant dynamical systems. Computer Methods in Applied Mechanics and Engineering. 384. 113973–113973. 1 indexed citations
14.
Parish, Eric & Kevin Carlberg. (2020). Time-series machine-learning error models for approximate solutions to parameterized dynamical systems. Computer Methods in Applied Mechanics and Engineering. 365. 112990–112990. 24 indexed citations
15.
Parish, Eric, et al.. (2018). A Residual-Based Petrov-Galerkin Reduced-Order Model with Memory Effects. arXiv (Cornell University). 4 indexed citations
16.
Parish, Eric, et al.. (2017). A prioriestimation of memory effects in reduced-order models of nonlinear systems using the Mori–Zwanzig formalism. Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences. 473(2205). 20170385–20170385. 34 indexed citations
17.
Parish, Eric, et al.. (2016). Towards a thorough use of the Mori-Zwanzig formalism for statistical coarse-graining of turbulent flows. Bulletin of the American Physical Society. 1 indexed citations
18.
Parish, Eric, Karthik Duraisamy, & Praveen Chandrashekar. (2016). Generalized Riemann problem-based upwind scheme for the vorticity transport equations. Computers & Fluids. 132. 10–18. 8 indexed citations
19.
Parish, Eric & Karthikeyan Duraisamy. (2016). Reduced Order Modeling of Turbulent Flows Using Statistical Coarse-graining. 46th AIAA Fluid Dynamics Conference. 7 indexed citations
20.
Parish, Eric & Karthik Duraisamy. (2015). A paradigm for data-driven predictive modeling using field inversion and machine learning. Journal of Computational Physics. 305. 758–774. 419 indexed citations breakdown →

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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