Richard P. Dwight

2.4k total citations
66 papers, 1.7k citations indexed

About

Richard P. Dwight is a scholar working on Computational Mechanics, Aerospace Engineering and Statistics, Probability and Uncertainty. According to data from OpenAlex, Richard P. Dwight has authored 66 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Computational Mechanics, 21 papers in Aerospace Engineering and 21 papers in Statistics, Probability and Uncertainty. Recurrent topics in Richard P. Dwight's work include Computational Fluid Dynamics and Aerodynamics (25 papers), Fluid Dynamics and Turbulent Flows (23 papers) and Probabilistic and Robust Engineering Design (20 papers). Richard P. Dwight is often cited by papers focused on Computational Fluid Dynamics and Aerodynamics (25 papers), Fluid Dynamics and Turbulent Flows (23 papers) and Probabilistic and Robust Engineering Design (20 papers). Richard P. Dwight collaborates with scholars based in Netherlands, Germany and France. Richard P. Dwight's co-authors include Paola Cinnella, Joël Brézillon, Jacques Peter, Wouter Edeling, H. Bijl, Mikael Kaandorp, Zhonghua Han, Fulvio Scarano, Andrea Sciacchitano and B.W. van Oudheusden and has published in prestigious journals such as Journal of Computational Physics, AIAA Journal and Computers & Geosciences.

In The Last Decade

Richard P. Dwight

64 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Richard P. Dwight Netherlands 20 1.2k 517 472 391 302 66 1.7k
Karthikeyan Duraisamy United States 18 1.1k 0.9× 464 0.9× 618 1.3× 199 0.5× 205 0.7× 65 1.4k
Thomas D. Economon United States 20 1.3k 1.1× 291 0.6× 898 1.9× 295 0.8× 140 0.5× 57 1.9k
Eusebio Valero Spain 25 1.2k 1.0× 429 0.8× 530 1.1× 169 0.4× 132 0.4× 111 1.7k
Jeff Borggaard United States 24 1.3k 1.2× 983 1.9× 209 0.4× 656 1.7× 142 0.5× 127 2.0k
Siva Nadarajah Canada 22 1.5k 1.3× 261 0.5× 716 1.5× 171 0.4× 91 0.3× 96 1.7k
Paola Cinnella France 24 1.8k 1.6× 500 1.0× 828 1.8× 375 1.0× 406 1.3× 160 2.5k
Trent Lukaczyk United States 11 793 0.7× 200 0.4× 624 1.3× 310 0.8× 97 0.3× 15 1.4k
Matthew Barone United States 22 956 0.8× 389 0.8× 934 2.0× 343 0.9× 449 1.5× 75 1.7k
Marian Nemec United States 24 1.4k 1.2× 165 0.3× 766 1.6× 224 0.6× 152 0.5× 62 1.8k
Krzysztof Fidkowski United States 21 2.3k 2.0× 564 1.1× 260 0.6× 172 0.4× 85 0.3× 111 2.5k

Countries citing papers authored by Richard P. Dwight

Since Specialization
Citations

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

Fields of papers citing papers by Richard P. Dwight

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Richard P. Dwight. 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 Richard P. Dwight. The network helps show where Richard P. Dwight may publish in the future.

Co-authorship network of co-authors of Richard P. Dwight

This figure shows the co-authorship network connecting the top 25 collaborators of Richard P. Dwight. A scholar is included among the top collaborators of Richard P. Dwight 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 Richard P. Dwight. Richard P. Dwight 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.
Singh, D. J., Richard P. Dwight, & Axelle Viré. (2024). Probabilistic surrogate modeling of damage equivalent loads on onshore and offshore wind turbines using mixture density networks. Wind energy science. 9(10). 1885–1904. 2 indexed citations
2.
Grabe, Cornelia, et al.. (2023). Data-driven augmentation of a RANS turbulence model for transonic flow prediction. International Journal of Numerical Methods for Heat & Fluid Flow. 33(4). 1544–1561. 7 indexed citations
3.
Viré, Axelle, et al.. (2020). Parametric slat design study for thick-base airfoils at high Reynolds numbers. Wind energy science. 5(3). 1075–1095. 8 indexed citations
4.
Dwight, Richard P., et al.. (2019). Machine Learning of Algebraic Stress Models using Deterministic Symbolic Regression. arXiv (Cornell University). 4 indexed citations
5.
Edeling, Wouter, et al.. (2018). Bayesian Predictions of Reynolds-Averaged Navier–Stokes Uncertainties Using Maximum a Posteriori Estimates. AIAA Journal. 56(5). 2018–2029. 42 indexed citations
6.
Dwight, Richard P., et al.. (2018). Data-Driven Deterministic Symbolic Regression of Nonlinear Stress-Strain Relation for RANS Turbulence Modelling. SPIRE - Sciences Po Institutional REpository. 3 indexed citations
7.
Dwight, Richard P., et al.. (2017). A Bayesian study of uncertainty in ultrasonic flow meters under non-ideal flow conditions. Metrologia. 54(4). 584–598. 10 indexed citations
8.
Dwight, Richard P., et al.. (2017). Uncertainty Reduction in Aeroelastic Systems with Time-Domain Reduced-Order Models. AIAA Journal. 55(7). 2437–2449. 9 indexed citations
9.
Koren, Barry, et al.. (2016). Non-intrusive uncertainty quantification using reduced cubature rules. Journal of Computational Physics. 332. 418–445. 9 indexed citations
10.
Schneiders, J.F.G., Stefan Pröbsting, Richard P. Dwight, B.W. van Oudheusden, & Fulvio Scarano. (2016). Pressure estimation from single-snapshot tomographic PIV in a turbulent boundary layer. Experiments in Fluids. 57(4). 39 indexed citations
11.
Edeling, Wouter, Richard P. Dwight, & Paola Cinnella. (2015). Simplex-stochastic collocation method with improved scalability. Journal of Computational Physics. 310. 301–328. 13 indexed citations
12.
Dwight, Richard P., et al.. (2015). Exploiting Adjoint Derivatives in High-Dimensional Metamodels. AIAA Journal. 53(5). 1391–1395. 21 indexed citations
13.
Edeling, Wouter, Paola Cinnella, & Richard P. Dwight. (2014). Predictive RANS simulations via Bayesian Model-Scenario Averaging. Journal of Computational Physics. 275. 65–91. 88 indexed citations
14.
Edeling, Wouter, Paola Cinnella, Richard P. Dwight, & H. Bijl. (2013). Bayesian estimates of parameter variability in the k–ε turbulence model. Journal of Computational Physics. 258. 73–94. 164 indexed citations
15.
Schneiders, J.F.G., Richard P. Dwight, & Fulvio Scarano. (2013). Vortex-in-Cell method for time-supersampling of PIV data. Research Repository (Delft University of Technology). 2 indexed citations
16.
Sciacchitano, Andrea, Richard P. Dwight, & Fulvio Scarano. (2012). Navier–Stokes simulations in gappy PIV data. Experiments in Fluids. 53(5). 1421–1435. 27 indexed citations
17.
Ronch, Andrea Da, Kenneth Badcock, Mehdi Ghoreyshi, et al.. (2010). Linear Frequency Domain and Harmonic Balance Predictions of Dynamic Derivatives. ePrints Soton (University of Southampton). 16 indexed citations
18.
Dwight, Richard P.. (2010). Bayesian inference for data assimilation using Least-Squares Finite Element methods. IOP Conference Series Materials Science and Engineering. 10. 12224–12224. 8 indexed citations
19.
Dwight, Richard P. & Joël Brézillon. (2006). Effect of Approximations of the Discrete Adjoint on Gradient-Based Optimization. AIAA Journal. 44(12). 3022–3031. 101 indexed citations
20.
Kroll, Norbert, Nicolas R. Gauger, Joël Brézillon, et al.. (2006). Flow simulation and shape optimization for aircraft design. Journal of Computational and Applied Mathematics. 203(2). 397–411. 20 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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