Surya R. Kalidindi
Affiliations: | 1992-2012 | Materials Science and Engineering (College of Engineering) | Drexel University, Philadelphia, PA, United States |
| 2013- | Mechanical Engineering | Georgia Institute of Technology, Atlanta, GA |
Area:
Materials Science Engineering, Biomedical EngineeringGoogle:
"Surya Kalidindi"Bio:
Kalidindi, Surya R. (Surya Raju) Polycrystal plasticity : constitutive modeling and deformation processing Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1992.
Parents
Sign in to add mentor| Lallit Anand | grad student | 1992 | MIT | |
| (Polycrystal plasticity : constitutive modeling and deformation processing) | ||||
Children
Sign in to add trainee| Abhishek Bhattacharyya | grad student | 2002 | Drexel |
| Ayman A. Salem | grad student | 2002 | Drexel |
| Anand Murugaiah | grad student | 2004 | Drexel |
| Joshua R. Houskamp | grad student | 2005 | Drexel |
| Xianping Wu | grad student | 2006 | Drexel |
| Hari K. Duvvuru | grad student | 2007 | Drexel |
| Sandip Basu | grad student | 2008 | Drexel |
| Massimiliano Binci | grad student | 2008 | Drexel |
| Aiguo Zhou | grad student | 2008 | Drexel |
| Marko Knezevic | grad student | 2009 | Drexel |
| Siddhartha Pathak | grad student | 2009 | Drexel |
| Giacomo Landi | grad student | 2011 | Drexel |
| Amanda J. Levinson | grad student | 2012 | Drexel |
| Naomi A. Hampson | grad student | 2013 | Drexel |
| Pranoy Ray | grad student | 2021-2026 | (Chemistry Tree) |
| Marat I. Latypov | post-doc | 2017 | Georgia Tech |
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Publications
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| Kalidindi SR. (2020) Feature engineering of material structure for AI-based materials knowledge systems Journal of Applied Physics. 128: 41103 |
| Yabansu YC, Altschuh P, Hötzer J, et al. (2020) A digital workflow for learning the reduced-order structure-property linkages for permeability of porous membranes Acta Materialia. 195: 668-680 |
| Castillo A, Kalidindi SR. (2019) A Bayesian Framework for the Estimation of the Single Crystal Elastic Parameters From Spherical Indentation Stress-Strain Measurements Frontiers in Materials. 6 |
| Bock FE, Aydin RC, Cyron CJ, et al. (2019) A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics Frontiers in Materials. 6 |
| Yabansu YC, Rehn V, Hötzer J, 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: 084006 |
| Montes de Oca Zapiain D, Kalidindi SR. (2019) Localization models for the plastic response of polycrystalline materials using the material knowledge systems framework Modelling and Simulation in Materials Science and Engineering. 27: 074008 |
| Paulson NH, Priddy MW, McDowell DL, et al. (2019) Reduced-order microstructure-sensitive protocols to rank-order the transition fatigue resistance of polycrystalline microstructures International Journal of Fatigue. 119: 1-10 |
| Latypov MI, Toth LS, Kalidindi SR. (2019) Materials knowledge system for nonlinear composites Computer Methods in Applied Mechanics and Engineering. 346: 180-196 |
| Yabansu YC, Iskakov A, Kapustina A, et al. (2019) Application of Gaussian process regression models for capturing the evolution of microstructure statistics in aging of nickel-based superalloys Acta Materialia. 178: 45-58 |
| Yang Z, Yabansu YC, Jha D, et al. (2019) Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches Acta Materialia. 166: 335-345 |