| Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets Z Yang, YC Yabansu, R Al-Bahrani, W Liao, AN Choudhary, SR Kalidindi, ... Computational Materials Science 151, 278-287, 2018 | 305 | 2018 |
| Material structure-property linkages using three-dimensional convolutional neural networks A Cecen, H Dai, YC Yabansu, SR Kalidindi, L Song Acta Materialia 146, 76-84, 2017 | 302 | 2017 |
| Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches Z Yang, YC Yabansu, D Jha, W Liao, AN Choudhary, SR Kalidindi, ... Acta Materialia 166, 335-345, 2019 | 157 | 2019 |
| Understanding and visualizing microstructure and microstructure variance as a stochastic process SR Niezgoda, YC Yabansu, SR Kalidindi Acta Materialia 59 (16), 6387-6400, 2011 | 148 | 2011 |
| Machine learning approaches for elastic localization linkages in high-contrast composite materials R Liu, YC Yabansu, A Agrawal, SR Kalidindi, AN Choudhary Integrating Materials and Manufacturing Innovation 4, 192-208, 2015 | 110 | 2015 |
| Extraction of reduced-order process-structure linkages from phase-field simulations YC Yabansu, P Steinmetz, J Hötzer, SR Kalidindi, B Nestler Acta Materialia 124, 182-194, 2017 | 106 | 2017 |
| Analytics for microstructure datasets produced by phase-field simulations P Steinmetz, YC Yabansu, J Hötzer, M Jainta, B Nestler, SR Kalidindi Acta Materialia 103, 192-203, 2016 | 88 | 2016 |
| Quantification and classification of microstructures in ternary eutectic alloys using 2-point spatial correlations and principal component analyses A Choudhury, YC Yabansu, SR Kalidindi, A Dennstedt Acta Materialia 110, 131-141, 2016 | 83 | 2016 |
| Calibrated localization relationships for elastic response of polycrystalline aggregates YC Yabansu, DK Patel, SR Kalidindi Acta Materialia 81, 151-160, 2014 | 83 | 2014 |
| Context aware machine learning approaches for modeling elastic localization in three-dimensional composite microstructures R Liu, YC Yabansu, Z Yang, AN Choudhary, SR Kalidindi, A Agrawal Integrating Materials and Manufacturing Innovation 6, 160-171, 2017 | 73 | 2017 |
| Application of Gaussian process regression models for capturing the evolution of microstructure statistics in aging of nickel-based superalloys YC Yabansu, A Iskakov, A Kapustina, S Rajagopalan, SR Kalidindi Acta Materialia 178, 45-58, 2019 | 71 | 2019 |
| Representation and calibration of elastic localization kernels for a broad class of cubic polycrystals YC Yabansu, SR Kalidindi Acta Materialia 94, 26-35, 2015 | 62 | 2015 |
| Application of Spherical Indentation and the Materials Knowledge System Framework to Establishing Microstructure-Yield Strength Linkages from Carbon Steel Scoops Excised from … A Iskakov, YC Yabansu, S Rajagopalan, A Kapustina, SR Kalidindi Acta Materialia 144, 758-767, 2017 | 56 | 2017 |
| Data Science Approaches for Microstructure Quantification and Feature Identification in Porous Membranes P Altschuh, YC Yabansu, J Hötzer, M Selzer, B Nestler, SR Kalidindi Journal of Membrane Science 540, 88-97, 2017 | 47 | 2017 |
| A new framework for rotationally invariant two-point spatial correlations in microstructure datasets A Cecen, YC Yabansu, SR Kalidindi Acta Materialia 158, 53-64, 2018 | 40 | 2018 |
| Application of Gaussian process autoregressive models for capturing the time evolution of microstructure statistics from phase-field simulations for sintering of … YC Yabansu, V Rehn, J Hötzer, B Nestler, SR Kalidindi Modelling and Simulation in Materials Science and Engineering 27 (8), 084006, 2019 | 25 | 2019 |
| A comparative study of the efficacy of local/global and parametric/nonparametric machine learning methods for establishing structure–property linkages in high-contrast 3D … P Fernandez-Zelaia, YC Yabansu, SR Kalidindi Integrating Materials and Manufacturing Innovation 8 (2), 67-81, 2019 | 20 | 2019 |
| A digital workflow for learning the reduced-order structure-property linkages for permeability of porous membranes YC Yabansu, P Altschuh, J Hötzer, M Selzer, B Nestler, SR Kalidindi Acta Materialia 195, 668-680, 2020 | 19 | 2020 |
| Evaluation of Ti–Mn alloys for additive manufacturing using high-throughput experimental assays and gaussian process regression X Gong, YC Yabansu, PC Collins, SR Kalidindi Materials 13 (20), 4641, 2020 | 14 | 2020 |
| High-throughput exploration of the process space in 18% Ni (350) maraging steels via spherical indentation stress–strain protocols and Gaussian process models S Parvinian, YC Yabansu, A Khosravani, H Garmestani, SR Kalidindi Integrating Materials and Manufacturing Innovation 9, 199-212, 2020 | 11 | 2020 |
Surya R. KalidindiGeorgia Institute of Technology, Atlanta, GA在 me.gatech.edu 的电子邮件经过验证
