| Universal physics-informed neural networks: symbolic differential operator discovery with sparse data L Podina, B Eastman, M Kohandel International Conference on Machine Learning, 27948-27956, 2023 | 13 | 2023 |
| Better peer grading through bayesian inference H Zarkoob, G d'Eon, L Podina, K Leyton-Brown Proceedings of the AAAI Conference on Artificial Intelligence 37 (5), 6137-6144, 2023 | 9 | 2023 |
| Cell viability prediction and optimization in extrusion-based bioprinting via neural network-based Bayesian optimization models D Mohammadrezaei, L Podina, J De Silva, M Kohandel Biofabrication 16 (2), 025016, 2024 | 8 | 2024 |
| A PINN approach to symbolic differential operator discovery with sparse data L Podina, B Eastman, M Kohandel arXiv preprint arXiv:2212.04630, 2022 | 5 | 2022 |
| A PINN Approach to Symbolic Differential Operator Discovery with Sparse Data B Eastman, L Podina, M Kohandel The Symbiosis of Deep Learning and Differential Equations II, 2022 | 2 | 2022 |
| Conformalized Physics-Informed Neural Networks L Podina, MT Rad, M Kohandel arXiv preprint arXiv:2405.08111, 2024 | 1 | 2024 |
| Learning Chemotherapy Drug Action via Universal Physics-Informed Neural Networks L Podina, A Ghodsi, M Kohandel arXiv preprint arXiv:2404.08019, 2024 | 1 | 2024 |
| Enhancing Symbolic Regression and Universal Physics-Informed Neural Networks with Dimensional Analysis L Podina, D Darooneh, J Grewal, M Kohandel arXiv preprint arXiv:2411.15919, 2024 | | 2024 |
| Denoising Diffusion Restoration Tackles Forward and Inverse Problems for the Laplace Operator A Mukherjee, MM Stadt, L Podina, M Kohandel, J Liu arXiv preprint arXiv:2402.08563, 2024 | | 2024 |
Mohammad KohandelProfessor, University of Waterloo在 uwaterloo.ca 的电子邮件经过验证
