Theron Rodgers

1.5k total citations
31 papers, 814 citations indexed

About

Theron Rodgers is a scholar working on Mechanical Engineering, Materials Chemistry and Automotive Engineering. According to data from OpenAlex, Theron Rodgers has authored 31 papers receiving a total of 814 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Mechanical Engineering, 12 papers in Materials Chemistry and 6 papers in Automotive Engineering. Recurrent topics in Theron Rodgers's work include Additive Manufacturing Materials and Processes (11 papers), Welding Techniques and Residual Stresses (7 papers) and Additive Manufacturing and 3D Printing Technologies (6 papers). Theron Rodgers is often cited by papers focused on Additive Manufacturing Materials and Processes (11 papers), Welding Techniques and Residual Stresses (7 papers) and Additive Manufacturing and 3D Printing Technologies (6 papers). Theron Rodgers collaborates with scholars based in United States, Switzerland and Austria. Theron Rodgers's co-authors include Jonathan D Madison, Veena Tikare, John Mitchell, Joseph E. Bishop, Ahmet Cecen, Surya R. Kalidindi, Mahon L. Maguire, Hojun Lim, Fadi Abdeljawad and Dan Bolintineanu and has published in prestigious journals such as Journal of Applied Physics, Acta Materialia and Journal of the American Ceramic Society.

In The Last Decade

Theron Rodgers

30 papers receiving 791 citations

Peers

Theron Rodgers

Jonathan D Madison United States

Patxi Fernandez-Zelaia United States

Ranadip Acharya United States

Elham Attar Germany

Frank Brueckner Germany

Chuanbao Jia China

Yuewei Ai China

Sergey Mekhontsev United States

Pınar Acar United States

H.J.M. Geijselaers Netherlands

Jonathan D Madison United States

Theron Rodgers

1.1k ×1.8

561 ×1.5

414 ×1.5

169 ×1.3

143 ×1.5

Citations per year, relative to Theron Rodgers Theron Rodgers (= 1×) peers Jonathan D Madison

Countries citing papers authored by Theron Rodgers

Since Specialization

Citations

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

Fields of papers citing papers by Theron Rodgers

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Theron Rodgers

This figure shows the co-authorship network connecting the top 25 collaborators of Theron Rodgers. A scholar is included among the top collaborators of Theron Rodgers 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 Theron Rodgers. Theron Rodgers 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

Liu, Dehao, et al.. (2025). GrainPaint: A multi-scale diffusion-based generative model for microstructure reconstruction of large-scale objects. Acta Materialia. 288. 120784–120784. 2 indexed citations

Wang, Zihan, et al.. (2025). Exascale granular microstructure reconstruction in 3D volumes of arbitrary geometries with generative learning. Acta Materialia. 289. 120859–120859. 1 indexed citations

Spangenberger, Anthony G., et al.. (2024). Part-scale microstructure prediction for laser powder bed fusion Ti-6Al-4V using a hybrid mechanistic and machine learning model. Additive manufacturing. 94. 104500–104500. 3 indexed citations

Rodgers, Theron, et al.. (2024). Solidification and crystallographic texture modeling of laser powder bed fusion Ti-6Al-4V using finite difference-monte carlo method. Materialia. 38. 102279–102279. 4 indexed citations

Zapiain, David Montes de Oca, Anh Tran, Nathan W. Moore, & Theron Rodgers. (2024). Calibration of thermal spray microstructure simulations using Bayesian optimization. Computational Materials Science. 235. 112845–112845. 2 indexed citations

Mitchell, John, Fadi Abdeljawad, Corbett Chandler. Battaile, et al.. (2023). Parallel simulation via SPPARKS of on-lattice kinetic and Metropolis Monte Carlo models for materials processing. Modelling and Simulation in Materials Science and Engineering. 31(5). 55001–55001. 24 indexed citations

Tran, Anh, et al.. (2023). Multi-fidelity Uncertainty Quantification for Homogenization Problems in Structure-Property Relationships from Crystal Plasticity Finite Elements. JOM. 76(6). 3007–3020. 2 indexed citations

Moore, Nathan W., J.L. Wise, Chad McCoy, et al.. (2023). Shock state distributions in porous tantalum and characterization with multipoint velocimetry. Journal of Applied Physics. 134(9). 1 indexed citations

Martinez, Carianne, Dan Bolintineanu, Theron Rodgers, et al.. (2023). Automated segmentation of porous thermal spray material CT scans with predictive uncertainty estimation. Computational Mechanics. 72(3). 525–551. 4 indexed citations

10.

Mitchell, John, et al.. (2023). A Bézier curve fit to melt pool geometry for modeling additive manufacturing microstructures. Computer Methods in Applied Mechanics and Engineering. 415. 116208–116208. 5 indexed citations

11.

Tran, Anh, Kathryn Maupin, & Theron Rodgers. (2022). Integrated computational materials engineering with monotonic Gaussian process.. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information).

12.

Tran, Anh, Kathryn Maupin, & Theron Rodgers. (2022). Monotonic Gaussian Process for Physics-Constrained Machine Learning With Materials Science Applications. Journal of Computing and Information Science in Engineering. 23(1). 7 indexed citations

13.

Rodgers, Theron, et al.. (2021). Fast three-dimensional rules-based simulation of thermal-sprayed microstructures. Computational Materials Science. 194. 110437–110437. 12 indexed citations

14.

Rodgers, Theron, Daniel Moser, Fadi Abdeljawad, et al.. (2021). Simulation of powder bed metal additive manufacturing microstructures with coupled finite difference-Monte Carlo method. Additive manufacturing. 41. 101953–101953. 62 indexed citations

15.

Olson, Aaron, et al.. (2021). Using Deep Neural Networks to Predict Material Types in Conditional Point Sampling Applied to Markovian Mixture Models. 346–357. 1 indexed citations

16.

Rodgers, Theron, et al.. (2017). Process-Structure Linkages Using a Data Science Approach: Application to Simulated Additive Manufacturing Data. Integrating materials and manufacturing innovation. 6(1). 54–68. 108 indexed citations

17.

Johnson, Kyle, Theron Rodgers, Jonathan D Madison, et al.. (2017). Simulation and experimental comparison of the thermo-mechanical history and 3D microstructure evolution of 304L stainless steel tubes manufactured using LENS. Computational Mechanics. 61(5). 559–574. 68 indexed citations

18.

Rodgers, Theron, Jonathan D Madison, & Veena Tikare. (2017). Simulation of metal additive manufacturing microstructures using kinetic Monte Carlo. Computational Materials Science. 135. 78–89. 278 indexed citations

19.

Rodgers, Theron, Jonathan D Madison, Veena Tikare, & Mahon L. Maguire. (2016). Predicting Mesoscale Microstructural Evolution in Electron Beam Welding. JOM. 68(5). 1419–1426. 60 indexed citations

20.

Thomas, Mda, et al.. (2003). THE EFFECT OF LITHIUM-BASED ADMIXTURES ON THE PROPERTIES OF CONCRETE. 2 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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