| Armdn: Associative and recurrent mixture density networks for eretail demand forecasting S Mukherjee, D Shankar, A Ghosh, N Tathawadekar, P Kompalli, ... arXiv preprint arXiv:1803.03800, 2018 | 36 | 2018 |
| Modeling of the nonlinear flame response of a Bunsen-type flame via multi-layer perceptron N Tathawadekar, NAK Doan, CF Silva, N Thuerey Proceedings of the Combustion Institute 38 (4), 6261-6269, 2021 | 26 | 2021 |
| Incomplete to complete multiphysics forecasting: a hybrid approach for learning unknown phenomena NN Tathawadekar, NAK Doan, CF Silva, N Thuerey Data-Centric Engineering 4, e27, 2023 | 6 | 2023 |
| Physical Quantities Reconstruction in Reacting Flows with Deep Learning N Tathawadekar, C Silva, P Sitte, NAK Doan INTER-NOISE and NOISE-CON Congress and Conference Proceedings 265 (6), 1645-1656, 2023 | 4 | 2023 |
| Linear and nonlinear flame response prediction of turbulent flames using neural network models N Tathawadekar, A Ösün, AJ Eder, CF Silva, N Thuerey International Journal of Spray and Combustion Dynamics 16 (3), 93-103, 2024 | 1 | 2024 |
| Modelling Flame Dynamics with Deep Learning Methods N Tathawadekar Technische Universität München, 2024 | | 2024 |
| Control of reacting flows with hybrid differentiable/deep learning flow solver N Tathawadekar, C Silva, N Thuerey, NAK Doan Bulletin of the American Physical Society, 2023 | | 2023 |
