| Deep learning methods for predicting fluid forces in dense particle suspensions NR Ashwin, Z Cao, N Muralidhar, D Tafti, A Karpatne Powder Technology 401, 117303, 2022 | 18 | 2022 |
| Comparison of reduced order models based on dynamic mode decomposition and deep learning for predicting chaotic flow in a random arrangement of cylinders NA Raj, D Tafti, N Muralidhar Physics of Fluids 35 (7), 2023 | 5 | 2023 |
| PhyFlow: Physics-Guided Deep Learning for Generating Interpretable 3D Flow Fields N Muralidhar, J Bu, Z Cao, N Raj, N Ramakrishnan, D Tafti, A Karpatne 2021 IEEE International Conference on Data Mining (ICDM), 1246-1251, 2021 | 3 | 2021 |
| Physics informed deep learning for flow and force predictions in dense ellipsoidal particle suspensions NR Ashwin, D Tafti, N Muralidhar, Z Cao Powder Technology, 119684, 2024 | | 2024 |
| Deep Learning for flow field and drag force predictions in dispersed particle flows N Raj, D Tafti, N Muralidhar Bulletin of the American Physical Society, 2023 | | 2023 |
| Comparative Study of Future State Predictions of Unsteady Multiphase Flows Using DMD and Deep Learning NA Raj, D Tafti, N Muralidhar, A Karpatne Conference on Fluid Mechanics and Fluid Power, 923-935, 2022 | | 2022 |
| Deep Learning Methods for Predicting Fluid Forces in Dense Particle Suspensions NA Raj Virginia Tech, 2021 | | 2021 |
| NETL WORKSHOP 2023 NA Raj, N Muralidhar, D Tafti | | |
Danesh TaftiMechanical Engineering Department, Virginia TechZweryfikowany adres z vt.edu
