Todd Oliver

1.6k total citations
39 papers, 1.1k citations indexed

About

Todd Oliver is a scholar working on Computational Mechanics, Aerospace Engineering and Mechanical Engineering. According to data from OpenAlex, Todd Oliver has authored 39 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Computational Mechanics, 9 papers in Aerospace Engineering and 7 papers in Mechanical Engineering. Recurrent topics in Todd Oliver's work include Fluid Dynamics and Turbulent Flows (16 papers), Computational Fluid Dynamics and Aerodynamics (15 papers) and Probabilistic and Robust Engineering Design (7 papers). Todd Oliver is often cited by papers focused on Fluid Dynamics and Turbulent Flows (16 papers), Computational Fluid Dynamics and Aerodynamics (15 papers) and Probabilistic and Robust Engineering Design (7 papers). Todd Oliver collaborates with scholars based in United States and Saudi Arabia. Todd Oliver's co-authors include Robert Moser, David Darmofal, Krzysztof Fidkowski, James Lu, Sai Hung Cheung, Serge Prudhomme, Ernesto E. Prudencio, Venkat Raman, David G. Bogard and Miloš Milosavljević and has published in prestigious journals such as Journal of Computational Physics, International Journal of Heat and Mass Transfer and Computer Methods in Applied Mechanics and Engineering.

In The Last Decade

Todd Oliver

36 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Todd Oliver United States 16 771 253 246 245 112 39 1.1k
Marc Gerritsma Netherlands 18 560 0.7× 144 0.6× 101 0.4× 107 0.4× 72 0.6× 59 899
Sean R. Copeland United States 6 712 0.9× 208 0.8× 475 1.9× 156 0.6× 78 0.7× 9 1.1k
S. S. Ravindran United States 15 881 1.1× 217 0.9× 147 0.6× 711 2.9× 39 0.3× 54 1.3k
James Reuther United States 20 1.0k 1.4× 324 1.3× 674 2.7× 173 0.7× 46 0.4× 48 1.6k
Ernesto E. Prudencio United States 13 238 0.3× 204 0.8× 122 0.5× 102 0.4× 75 0.7× 28 603
Jean‐Yves Trépanier Canada 23 1.3k 1.7× 115 0.5× 414 1.7× 99 0.4× 48 0.4× 122 1.8k
Peter Sturdza United States 10 359 0.5× 148 0.6× 274 1.1× 102 0.4× 24 0.2× 15 807
J.‐V. Romero Spain 14 232 0.3× 146 0.6× 78 0.3× 89 0.4× 55 0.5× 61 699
A.G. Buchan United Kingdom 12 393 0.5× 131 0.5× 216 0.9× 441 1.8× 73 0.7× 24 704
Carsten Othmer Germany 19 455 0.6× 84 0.3× 221 0.9× 141 0.6× 91 0.8× 40 997

Countries citing papers authored by Todd Oliver

Since Specialization
Citations

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

Fields of papers citing papers by Todd Oliver

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Todd Oliver

This figure shows the co-authorship network connecting the top 25 collaborators of Todd Oliver. A scholar is included among the top collaborators of Todd Oliver 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 Todd Oliver. Todd Oliver 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.
Oliver, Todd, et al.. (2025). Characterization of uncertainties in electron-argon collision cross sections. Plasma Sources Science and Technology. 34(2). 25009–25009.
2.
Michoski, Craig, Todd Oliver, D. R. Hatch, et al.. (2024). A Gaussian process guide for signal regression in magnetic fusion. Nuclear Fusion. 64(3). 35001–35001. 4 indexed citations
3.
Oliver, Todd, et al.. (2024). Automated Bayesian high-throughput estimation of plasma temperature and density from emission spectroscopy. Review of Scientific Instruments. 95(7). 1 indexed citations
4.
Oliver, Todd, et al.. (2024). A second-order-in-time, explicit approach addressing the redundancy in the low-Mach, variable-density Navier-Stokes equations. Journal of Computational Physics. 514. 113216–113216.
5.
Oliver, Todd, et al.. (2022). Experimental and Computational Investigation of Integrated Internal and Film Cooling Designs Incorporating a Thermal Barrier Coating. Journal of Turbomachinery. 144(9). 10 indexed citations
6.
Wu, Chengyue, David A. Hormuth, Todd Oliver, et al.. (2020). Patient-Specific Characterization of Breast Cancer Hemodynamics Using Image-Guided Computational Fluid Dynamics. IEEE Transactions on Medical Imaging. 39(9). 2760–2771. 24 indexed citations
7.
Michoski, Craig, Miloš Milosavljević, Todd Oliver, & D. R. Hatch. (2020). Solving differential equations using deep neural networks. Neurocomputing. 399. 193–212. 70 indexed citations
8.
Oliver, Todd, David G. Bogard, & Robert Moser. (2019). Large eddy simulation of compressible, shaped-hole film cooling. International Journal of Heat and Mass Transfer. 140. 498–517. 41 indexed citations
9.
Michoski, Craig, Julian Kates‐Harbeck, N.C. Logan, et al.. (2018). Quantifying and Propagating Uncertainties to Enhance Real-time Disruption Prediction with Machine Learning. Bulletin of the American Physical Society. 2018. 1 indexed citations
10.
Oliver, Todd, et al.. (2017). A mass-conserving mixed Fourier-Galerkin B-Spline-collocation method for Direct Numerical Simulation of the variable-density Navier-Stokes equations. Bulletin of the American Physical Society. 1 indexed citations
11.
Sondak, David, Todd Oliver, Chris Simmons, & Robert Moser. (2017). An Inadequacy Formulation for an Uncertain Flamelet Model. 1 indexed citations
12.
Ghanem, Roger, Vahid Keshavarzzadeh, Sami F. Masri, et al.. (2014). Probabilistic Approach to NASA Langley Research Center Multidisciplinary Uncertainty Quantification Challenge Problem. Journal of Aerospace Information Systems. 12(1). 170–188. 15 indexed citations
13.
Kirk, Benjamin, et al.. (2013). Recent Advancements in Fully Implicit Numerical Methods for Hypersonic Reacting Flows. Scopus. 1 indexed citations
14.
Oliver, Todd, et al.. (2013). A Semi-Implicit, Fourier-Galerkin/B-Spline Collocation Approach for DNS of Compressible, Reacting, Wall-Bounded Flow. Bulletin of the American Physical Society. 1 indexed citations
15.
16.
Oliver, Todd, et al.. (2013). Bayesian analysis of syngas chemistry models. Combustion Theory and Modelling. 17(5). 858–887. 54 indexed citations
17.
Oliver, Todd, et al.. (2012). Bayesian methods for the quantification of uncertainties in syngas chemistry models. 50th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition. 2 indexed citations
18.
Oliver, Todd & Robert Moser. (2009). UNCERTAINTY QUANTIFICATION FOR RANS TURBULENCE MODEL PREDICTIONS. Bulletin of the American Physical Society. 62. 7 indexed citations
19.
Oliver, Todd & David Darmofal. (2009). Analysis of Dual Consistency for Discontinuous Galerkin Discretizations of Source Terms. SIAM Journal on Control and Optimization. 1 indexed citations
20.
Baker, J., et al.. (2001). Correlations of Critical Froude Number for Annular-Rimming Flow in Rotating Heat Pipes. Journal of Fluids Engineering. 123(4). 909–913. 11 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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