Ankit Agrawal, Ph.D.
Affiliations: | 2009 | Computer Science | Iowa State University, Ames, IA, United States |
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"Ankit Agrawal"Parents
Sign in to add mentor| Xiaoqiu Huang | grad student | 2009 | Iowa State | |
| (Sequence-specific sequence comparison using pairwise statistical significance.) | ||||
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Publications
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| Jha D, Choudhary K, Tavazza F, et al. (2020) Author Correction: Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning. Nature Communications. 11: 3643 |
| Yang Z, Papanikolaou S, Reid ACE, et al. (2020) Learning to Predict Crystal Plasticity at the Nanoscale: Deep Residual Networks and Size Effects in Uniaxial Compression Discrete Dislocation Simulations. Scientific Reports. 10: 8262 |
| Jha D, Choudhary K, Tavazza F, et al. (2019) Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning. Nature Communications. 10: 5316 |
| Paul A, Furmanchuk A, Liao WK, et al. (2019) Property Prediction of Organic Donor Molecules for Photovoltaic Applications Using Extremely Randomized Trees. Molecular Informatics |
| Paul A, Acar P, Liao W, et al. (2019) Microstructure optimization with constrained design objectives using machine learning-based feedback-aware data-generation Computational Materials Science. 160: 334-351 |
| 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 |
| Jha D, Ward L, Paul A, et al. (2018) ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition. Scientific Reports. 8: 17593 |
| Yang Z, Li X, Catherine Brinson L, et al. (2018) Microstructural Materials Design Via Deep Adversarial Learning Methodology Journal of Mechanical Design. 140 |
| Agrawal A, Choudhary A. (2018) An online tool for predicting fatigue strength of steel alloys based on ensemble data mining International Journal of Fatigue. 113: 389-400 |
| Yang Z, Yabansu YC, Al-Bahrani R, et al. (2018) Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets Computational Materials Science. 151: 278-287 |