Eric Parish

1.0k citations
27 papers · 648 · 1 hit paper · h-index 9

Impact in

Papers in

Eric Parish

27 papers receiving 613 citations

Eric Parish's Hit Papers

A paradigm for data-driven predictive modeling using field inversion and machine learning 2015 · 419 citations
4190+3+7Years since publication100200300400

Peers

Eric Parish
Comparison fields: 5 of 70
  • Statistical and Nonlinear Physics 404
  • Computational Mechanics 419
  • Statistics, Probability and Uncertainty 113
  • Aerospace Engineering 151
  • Environmental Engineering 69
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Deep Ray United States
Gahl Berkooz Sweden
A.G. Buchan United Kingdom
Konstantin Afanasiev Germany
Laurent Cordier France
Marek Morzyński Poland
Nicholas Geneva United States
Andrea Mola Italy
Michel Bergmann France
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Citations per field
00.5×11.3×
Deep Ray · 1×
Citations per year

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-authors

The 17 scholars most cited alongside Eric Parish, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Eric Parish Line = papers co-authored together Eric Parish links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 27 papers — load more, or switch the sort, to bring in the rest.

#Work
1
A paradigm for data-driven predictive modeling using field inversion and machine learning
Hit paper breakdown →
2015419
2 201734
3 201734
4 202132
5 202024
6 201513
7 202111
8 202310
9 20259
10 20168
11 20167
12 20247
13 20236
14 20226
15 20225
16 20234
17
A Residual-Based Petrov-Galerkin Reduced-Order Model with Memory Effects
20184
18 20243
19 20242
20 20242

About Eric Parish

Eric Parish is a scholar working on Statistical and Nonlinear Physics, Computational Mechanics, Statistics, Probability and Uncertainty, Environmental Engineering and Numerical Analysis, having authored 27 papers that have together received 648 indexed citations. Recurring topics across this work include Model Reduction and Neural Networks (20 papers), Fluid Dynamics and Turbulent Flows (10 papers), Computational Fluid Dynamics and Aerodynamics (10 papers), Probabilistic and Robust Engineering Design (9 papers), Numerical methods for differential equations (4 papers), Wind and Air Flow Studies (4 papers), Fluid Dynamics and Vibration Analysis (3 papers) and Nuclear reactor physics and engineering (2 papers). The work is most often cited by research in Statistical and Nonlinear Physics (404 citations), Computational Mechanics (419 citations), Statistics, Probability and Uncertainty (113 citations), Aerospace Engineering (151 citations) and Environmental Engineering (69 citations). Eric Parish has collaborated with scholars based in United States, Canada and India. Frequent co-authors include Karthik Duraisamy, Kookjin Lee, Kevin Carlberg, Karthikeyan Duraisamy, Francesco Rizzi, Patrick Blonigan, Praveen Chandrashekar, Masayuki Yano, Traian Iliescu and Francesco Rizzi. Their work appears in journals such as Computer Methods in Applied Mechanics and Engineering, Journal of Computational Physics, Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences, Computational Mechanics and Physics of Fluids.

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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