Surya R. Kalidindi

Materials Chemistry · 2000–2019

A researcher in the Science corpus: 36 papers; trusts 57 researchers and is trusted by 49 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 162 citations into it; #345 of 11,513 by trust here, #1,462 by citations
• Mechanics of Materials — 136 citations into it; #348 of 6,291 by trust here, #230 by citations
• Mechanical Engineering — 101 citations into it; #219 of 8,300 by trust here, #600 by citations
• Biomaterials — 35 citations into it; #2,117 of 2,152 by trust here, #951 by citations
• Biomedical Engineering — 35 citations into it
• Computer Vision and Pattern Recognition — 31 citations into it
• Atomic and Molecular Physics, and Optics — 30 citations into it; #6,185 of 7,135 by trust here, #5,007 by citations
• Computational Theory and Mathematics — 26 citations into it

Whom Surya R. Kalidindi trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. S. Niezgoda
2. David L. McDowell
3. Graeme W. Milton
4. David T. Fullwood
5. Ricardo A. Lebensohn
6. B. Adams
7. A. Fischer-Cripps
8. Chih-Jen Lin
9. A. Salem
10. Salvatore Torquato

Who trusts Surya R. Kalidindi overall

1. Franz Roters
2. Philip Eisenlohr
3. Dierk Raabe
4. David L. McDowell
5. S. Wong
6. Marko Knezevic
7. Irene J. Beyerlein
8. C. Tomé
9. Noah H. Paulson
10. T. Bieler

Papers from Surya R. Kalidindi's perspective

Ordered by how strongly the researchers Surya R. Kalidindi trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Gradient-based learning applied to document recognition
3. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
4. GENERATIVE ADVERSARIAL NETS
5. Rectified Linear Units Improve Restricted Boltzmann Machines
6. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
7. Rethinking the Inception Architecture for Computer Vision
8. Understanding the difficulty of training deep feedforward neural networks
9. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
10. Long Short-Term Memory

Their papers

• Finite element modeling of crystal plasticity with grains shaped as truncated octahedrons (2006) — 170 citations
• Determination of the effective zero-point and the extraction of spherical nanoindentation stress–strain curves (2008) — 254 citations
• Modeling anisotropic strain hardening and deformation textures in low stacking fault energy fcc metals (2001) — 228 citations
• Comparison of two grain interaction models for polycrystal plasticity and deformation texture prediction (2002) — 148 citations
• Understanding and visualizing microstructure and microstructure variance as a stochastic process (2011) — 145 citations
• Key computational modeling issues in Integrated Computational Materials Engineering (2013) — 347 citations
• Strain hardening due to deformation twinning in α-titanium: Mechanisms (2006) — 253 citations
• Crystal plasticity simulations using discrete Fourier transforms (2009) — 150 citations
• Deformation twinning in AZ31: Influence on strain hardening and texture evolution (2010) — 623 citations
• Work-hardening/softening behaviour of b.c.c. polycrystals during changing strain paths: I. An integrated model based on substructure and texture evolution, and its prediction of the stress–strain behaviour of an IF steel during two-stage strain paths (2001) — 234 citations
• Role of Deformation Twinning on Strain Hardening in Cubic and Hexagonal Polycrystalline Metals (2003) — 140 citations
• Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets (2018) — 343 citations
• Material structure-property linkages using three-dimensional convolutional neural networks (2018) — 299 citations
• Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches (2019) — 170 citations
• Prediction of crystallographic texture evolution and anisotropic stress–strain curves during large plastic strains in high purity α-titanium using a Taylor-type crystal plasticity model (2007) — 232 citations
• Microstructure Sensitive Design for Performance Optimization (2012) — 458 citations
• Strain hardening regimes and microstructure evolution during large strain compression of high purity titanium (2002) — 157 citations
• Materials informatics (2018) — 338 citations
• Structure–property linkages using a data science approach: Application to a non-metallic inclusion/steel composite system (2015) — 186 citations
• Influence of deformation twinning on static annealing of AZ31 Mg alloy (2013) — 185 citations