| 3D Grain Shape Generation in Polycrystals Using Generative Adversarial Networks DK Jangid, NR Brodnik, A Khan, MG Goebel, MLP Echlin, TM Pollock, ... Integrating Materials and Manufacturing Innovation 11 (1), 71-84, 2022 | 12 | 2022 |
| Adaptable physics-based super-resolution for electron backscatter diffraction maps DK Jangid, NR Brodnik, MG Goebel, A Khan, SS Majeti, MLP Echlin, ... npj Computational Materials 8 (1), 255, 2022 | 8 | 2022 |
| Q-RBSA: high-resolution 3D EBSD map generation using an efficient quaternion transformer network DK Jangid, NR Brodnik, MLP Echlin, C Gudavalli, C Levenson, ... npj Computational Materials 10 (1), 27, 2024 | 4 | 2024 |
| 3DMaterialGAN: Learning 3D Shape Representation from Latent Space for Materials Science Applications DK Jangid, NR Brodnik, A Khan, MLP Echlin, TM Pollock, S Daly, ... arXiv preprint arXiv:2007.13887, 2020 | 3 | 2020 |
| Titanium 3D Microstructure for Physics-based Generative Models: A Dataset and Primer DK Jangid, NR Brodnik, MLP Echlin, SH Daly, TM Pollock, BS Manjunath International Conference on Machine Learning Workshop, (https://syns-ml …, 2023 | 2 | 2023 |
| Machine learning-based approaches for synthetic training data generation and image sharpening DK Jangid, JS Lee, HR Sheikh US Patent App. 17/820,795, 2024 | | 2024 |
| Supplementary Material: Q-RBSA: high-resolution 3D EBSD map generation using an efficient quaternion transformer network DK Jangid, NR Brodnik, MLP Echlin, TM Pollock, SH Daly, BS Manjunath npj Computationals Materials (https://static-content.springer.com/esm/art …, 2024 | | 2024 |
