Surya R. Kalidindi

25.2k total citations · 7 hit papers
329 papers, 20.6k citations indexed

About

Surya R. Kalidindi is a scholar working on Materials Chemistry, Mechanics of Materials and Mechanical Engineering. According to data from OpenAlex, Surya R. Kalidindi has authored 329 papers receiving a total of 20.6k indexed citations (citations by other indexed papers that have themselves been cited), including 183 papers in Materials Chemistry, 179 papers in Mechanics of Materials and 135 papers in Mechanical Engineering. Recurrent topics in Surya R. Kalidindi's work include Microstructure and mechanical properties (70 papers), Metal and Thin Film Mechanics (51 papers) and Composite Material Mechanics (50 papers). Surya R. Kalidindi is often cited by papers focused on Microstructure and mechanical properties (70 papers), Metal and Thin Film Mechanics (51 papers) and Composite Material Mechanics (50 papers). Surya R. Kalidindi collaborates with scholars based in United States, Germany and Belgium. Surya R. Kalidindi's co-authors include R.D. Doherty, Lallit Anand, Ayman A. Salem, Michel W. Barsoum, Stephen R. Niezgoda, Curt A. Bronkhorst, Siddhartha Pathak, Marko Knežević, Ehab A. El‐Danaf and David T. Fullwood and has published in prestigious journals such as Physical Review Letters, Advanced Materials and Nature Materials.

In The Last Decade

Surya R. Kalidindi

323 papers receiving 20.1k citations

Hit Papers

Crystallographic texture evolution in bulk deformation pr... 1992 2026 2003 2014 1992 2010 1998 1999 1997 250 500 750
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. Crystallographic texture evolution in bulk deformation processing of FCC metals
    1992 997 citations Surya R. Kalidindi, Curt A. Bronkhorst et al. profile →
  2. Deformation twinning in AZ31: Influence on strain hardening and texture evolution
    2010 556 citations Marko Knežević, Surya R. Kalidindi et al. Acta Materialia profile →
  3. Incorporation of deformation twinning in crystal plasticity models
    1998 470 citations Surya R. Kalidindi profile →
  4. Influence of grain size and stacking-fault energy on deformation twinning in fcc metals
    1999 428 citations Surya R. Kalidindi et al. profile →
  5. Strain hardening regimes and microstructural evolution during large strain compression of low stacking fault energy fcc alloys that form deformation twins
    1997 415 citations Surya R. Kalidindi et al. profile →
  6. A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics
    2019 294 citations Surya R. Kalidindi et al. profile →
  7. Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets
    2018 269 citations Yuksel C. Yabansu, Surya R. Kalidindi et al. Computational Materials Science profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Surya R. Kalidindi United States 78 13.4k 11.7k 8.5k 2.2k 1.7k 329 20.6k
Sybrand van der Zwaag Netherlands 71 10.8k 0.8× 11.8k 1.0× 4.3k 0.5× 964 0.4× 2.6k 1.5× 599 20.5k
W.A. Curtin United States 78 10.6k 0.8× 13.0k 1.1× 6.1k 0.7× 2.8k 1.3× 1.7k 1.0× 289 20.9k
Xiaoxu Huang China 63 12.3k 0.9× 13.2k 1.1× 4.5k 0.5× 2.0k 0.9× 773 0.4× 394 18.1k
Javier LLorca Spain 74 6.1k 0.5× 9.5k 0.8× 7.8k 0.9× 2.5k 1.2× 1.3k 0.8× 358 17.0k
Yuri Estrin Australia 68 13.4k 1.0× 14.3k 1.2× 6.2k 0.7× 2.7k 1.2× 1.4k 0.8× 334 19.1k
Éric Maire France 58 4.4k 0.3× 8.0k 0.7× 3.9k 0.5× 1.1k 0.5× 2.8k 1.6× 314 14.5k
David L. McDowell United States 73 10.2k 0.8× 10.8k 0.9× 8.6k 1.0× 548 0.3× 1.0k 0.6× 391 17.4k
M.F. Horstemeyer United States 65 6.5k 0.5× 8.2k 0.7× 4.4k 0.5× 2.7k 1.3× 1.2k 0.7× 378 13.8k
Tresa M. Pollock United States 75 10.0k 0.7× 20.3k 1.7× 5.5k 0.7× 2.3k 1.1× 3.6k 2.1× 459 25.1k
R.J. Asaro United States 51 9.2k 0.7× 8.6k 0.7× 8.2k 1.0× 663 0.3× 1.3k 0.7× 157 14.9k

Countries citing papers authored by Surya R. Kalidindi

Since Specialization
Citations

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

Fields of papers citing papers by Surya R. Kalidindi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Surya R. Kalidindi. 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 Surya R. Kalidindi. The network helps show where Surya R. Kalidindi may publish in the future.

Co-authorship network of co-authors of Surya R. Kalidindi

This figure shows the co-authorship network connecting the top 25 collaborators of Surya R. Kalidindi. A scholar is included among the top collaborators of Surya R. Kalidindi 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 Surya R. Kalidindi. Surya R. Kalidindi 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.
Kalidindi, Surya R., et al.. (2025). Thermodynamically-Informed Iterative Neural Operators for heterogeneous elastic localization. Computer Methods in Applied Mechanics and Engineering. 441. 117939–117939. 1 indexed citations
2.
Kalidindi, Surya R., et al.. (2025). A texture-dependent yield criterion based on Support Vector Classification. International Journal of Plasticity. 188. 104311–104311. 1 indexed citations
3.
Olsen, Daniel H., et al.. (2025). Batch active learning for microstructure–property relations in energetic materials. Mechanics of Materials. 205. 105308–105308. 1 indexed citations
4.
5.
McDowell, David L., et al.. (2024). Bayesian protocols for high-throughput identification of kinematic hardening model forms. International Journal of Mechanical Sciences. 285. 109791–109791.
6.
Kalidindi, Surya R., et al.. (2024). Inverse stochastic microstructure design. Acta Materialia. 271. 119877–119877. 15 indexed citations
7.
Kalidindi, Surya R., et al.. (2023). Local–Global Decompositions for Conditional Microstructure Generation. Acta Materialia. 253. 118966–118966. 14 indexed citations
8.
Gissinger, Jacob R., et al.. (2023). Voxelized atomic structure framework for materials design and discovery. Computational Materials Science. 230. 112431–112431. 5 indexed citations
9.
Hardin, James O., et al.. (2022). Machine learning-enabled feature classification of evaporation-driven multi-scale 3D printing. Flexible and Printed Electronics. 7(1). 14011–14011. 7 indexed citations
10.
Sheridan, Richard J., et al.. (2022). Snappy: A New Automated Testing Machine for Monitoring the Break Evolution Process during Single Fiber Fragmentation Test. Experimental Techniques. 47(5). 1073–1084. 4 indexed citations
11.
Wheeler, Daniel, et al.. (2022). How important is microstructural feature selection for data-driven structure-property mapping?. MRS Communications. 12(1). 95–103. 9 indexed citations
12.
Kalidindi, Surya R., et al.. (2021). Decoding defect statistics from diffractograms via machine learning. npj Computational Materials. 7(1). 13 indexed citations
13.
Yabansu, Yuksel C., et al.. (2019). Application of Gaussian process autoregressive models for capturing the time evolution of microstructure statistics from phase-field simulations for sintering of polycrystalline ceramics. Modelling and Simulation in Materials Science and Engineering. 27(8). 84006–84006. 22 indexed citations
14.
Khosravani, Ali, Lutz Morsdorf, Cemal Cem Taşan, & Surya R. Kalidindi. (2018). Multiresolution mechanical characterization of hierarchical materials: Spherical nanoindentation on martensitic Fe-Ni-C steels. Acta Materialia. 153. 257–269. 27 indexed citations
15.
Wargo, Eric A., et al.. (2011). A Representative Volume Element Approach for Pore-Scale Modeling of Fuel Cell Materials. ECS Transactions. 41(1). 131–139. 2 indexed citations
16.
Kalidindi, Surya R., et al.. (2010). Thermo-Elastic Localization Relationships for Multi-Phase Composites. Cmc-computers Materials & Continua. 16(3). 273–294. 20 indexed citations
17.
Alharbi, Hamad F., Marko Knežević, & Surya R. Kalidindi. (2010). Spectral Approaches for the Fast Computation of Yield Surfaces and First-Order Plastic Property Closures for Polycrystalline Materials with Cubic-Triclinic Textures. Cmc-computers Materials & Continua. 15(2). 153–172. 62 indexed citations
18.
Fullwood, David T., Surya R. Kalidindi, Brent L. Adams, & S.N. Moghaddas Tafreshi. (2009). A Discrete Fourier Transform Framework for Localization Relations. Cmc-computers Materials & Continua. 9(1). 25–40. 9 indexed citations
19.
Niezgoda, Stephen R. & Surya R. Kalidindi. (2009). Applications of the Phase-Coded Generalized Hough Transform to Feature Detection, Analysis, and Segmentation of Digital Microstructures. Cmc-computers Materials & Continua. 14(2). 79–98. 17 indexed citations
20.
Bronkhorst, Curt A., Surya R. Kalidindi, & Lallit Anand. (1992). Polycrystalline plasticity and the evolution of crystallographic texture in FCC metals. Philosophical Transactions of the Royal Society of London Series A Physical and Engineering Sciences. 341(1662). 443–477. 342 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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