| Fourier Spectrum Discrepancies in Deep Network Generated Images T Dzanic, K Shah, F Witherden Advances in Neural Information Processing Systems 33 (NeurIPS 2020), 2019 | 139 | 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 | 80 | 2022 |
| Physics-Informed Neural Networks as Solvers for the Time-Dependent Schr\" odinger Equation K Shah, P Stiller, N Hoffmann, A Cangi Machine Learning and the Physical Sciences Workshop at the 36th conference …, 2022 | 6 | 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 |
| Accelerating Electron Dynamics Simulations through Machine Learned Time Propagators K Shah, A Cangi arXiv preprint arXiv:2407.09628, 2024 | 1 | 2024 |
| 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 | 1 | 2024 |
| Machine-Learning for Static and Dynamic Electronic Structure Theory L Fiedler, K Shah, A Cangi Machine Learning in Molecular Sciences, 113-160, 2023 | 1 | 2023 |
| Materials Learning Algorithms (MALA): Scalable Machine Learning for Electronic Structure Calculations in Large-Scale Atomistic Simulations A Cangi, L Fiedler, B Brzoza, K Shah, TJ Callow, D Kotik, S Schmerler, ... arXiv preprint arXiv:2411.19617, 2024 | | 2024 |
| Data science education in undergraduate physics: Lessons learned from a community of practice K Shah, J Butler, AV Knaub, A Zenginoğlu, W Ratcliff, M Soltanieh-Ha American Journal of Physics 92 (9), 655-662, 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 |
| 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 |
| Training Dynamic Neural Networks K Shah | | |
| Deep learning and the Schrödinger equation K SHAH | | |
Attila CangiCenter for Advanced Systems Understanding, Helmholtz-Zentrum Dresden-RossendorfVerified email at hzdr.de