| Fourier Spectrum Discrepancies in Deep Network Generated Images T Dzanic, K Shah, F Witherden arXiv preprint arXiv:1911.06465, 2019 | 112 | 2019 |
| Deep dive into machine learning density functional theory for materials science and chemistry L Fiedler, K Shah, M Bussmann, A Cangi Physical Review Materials 6 (4), 040301, 2022 | 49 | 2022 |
| Physics-Informed Neural Networks as Solvers for the Time-Dependent Schr\" odinger Equation K Shah, P Stiller, N Hoffmann, A Cangi arXiv preprint arXiv:2210.12522, 2022 | 3 | 2022 |
| Inferring student success predictors for CS1301x online course at Georgia Tech K Shah, M Bach, N Qin, A Liu, H Hussen, JY Lee, R Kadel Poster session presented at the American Society of Engineering Education …, 2017 | 2 | 2017 |
| Inverting the Kohn–Sham equations with physics-informed machine learning V Martinetto, K Shah, A Cangi, A Pribram-Jones Machine Learning: Science and Technology 5 (1), 015050, 2024 | | 2024 |
| Physics-Informed Machine Learning for Addressing Challenges in Static and Time-Dependent Density Functional Theory K Shah, A Cangi Bulletin of the American Physical Society, 2024 | | 2024 |
| Data Science Education in Undergraduate Physics: Lessons Learned from a Community of Practice K Shah, J Butler, A Knaub, A Zenginoğlu, W Ratcliff, M Soltanieh-ha arXiv preprint arXiv:2403.00961, 2024 | | 2024 |
| Machine-Learning for Static and Dynamic Electronic Structure Theory L Fiedler, K Shah, A Cangi Machine Learning in Molecular Sciences, 113-160, 2023 | | 2023 |
| Accelerating Time-Dependent Density Functional Theory with Physics-Informed Neural Networks K Shah, A Cangi APS March Meeting Abstracts 2022, M01. 007, 2022 | | 2022 |
| Hierarchical Bayesian Modeling K Shah Bulletin of the American Physical Society 63, 2018 | | 2018 |
| Uncertainty Quantification of Machine Learned Density Functionals K Shah Georgia Institute of Technology, 2018 | | 2018 |
Attila CangiCenter for Advanced Systems Understanding, Helmholtz-Zentrum Dresden-RossendorfVerified email at hzdr.de
