| DiscretizationNet: A machine-learning based solver for Navier–Stokes equations using finite volume discretization R Ranade, C Hill, J Pathak Computer Methods in Applied Mechanics and Engineering 378, 113722, 2021 | 113 | 2021 |
| An ANN based hybrid chemistry framework for complex fuels R Ranade, S Alqahtani, A Farooq, T Echekki Fuel 241, 625-636, 2019 | 58 | 2019 |
| Algorithmically-consistent deep learning frameworks for structural topology optimization J Rade, A Balu, E Herron, J Pathak, R Ranade, S Sarkar, A Krishnamurthy Engineering Applications of Artificial Intelligence 106, 104483, 2021 | 44* | 2021 |
| A framework for data-based turbulent combustion closure: A posteriori validation R Ranade, T Echekki Combustion and flame 210, 279-291, 2019 | 44 | 2019 |
| An efficient machine-learning approach for PDF tabulation in turbulent combustion closure R Ranade, G Li, S Li, T Echekki Combustion Science and Technology 193 (7), 1258-1277, 2021 | 35 | 2021 |
| A framework for data-based turbulent combustion closure: A priori validation R Ranade, T Echekki Combustion and Flame 206, 490-505, 2019 | 34 | 2019 |
| An extended hybrid chemistry framework for complex hydrocarbon fuels R Ranade, S Alqahtani, A Farooq, T Echekki Fuel 251, 276-284, 2019 | 27 | 2019 |
| Investigation of deep learning methods for efficient high-fidelity simulations in turbulent combustion KM Gitushi, R Ranade, T Echekki Combustion and Flame 236, 111814, 2022 | 24 | 2022 |
| A hybrid iterative numerical transferable solver (HINTS) for PDEs based on deep operator network and relaxation methods E Zhang, A Kahana, E Turkel, R Ranade, J Pathak, GE Karniadakis arXiv preprint arXiv:2208.13273, 2022 | 17 | 2022 |
| Generalized joint probability density function formulation inturbulent combustion using deeponet R Ranade, K Gitushi, T Echekki arXiv preprint arXiv:2104.01996, 2021 | 14 | 2021 |
| A thermal machine learning solver for chip simulation R Ranade, H He, J Pathak, N Chang, A Kumar, J Wen Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD, 111-117, 2022 | 10 | 2022 |
| One-shot learning for solution operators of partial differential equations A Jiao, H He, R Ranade, J Pathak, L Lu arXiv preprint arXiv:2104.05512, 2021 | 10 | 2021 |
| A composable machine-learning approach for steady-state simulations on high-resolution grids R Ranade, C Hill, L Ghule, J Pathak Advances in Neural Information Processing Systems, 2022, 2022 | 9* | 2022 |
| Diffusion model based data generation for partial differential equations R Apte, S Nidhan, R Ranade, J Pathak arXiv preprint arXiv:2306.11075, 2023 | 5 | 2023 |
| Experiment-based modeling of turbulent flames with inhomogeneous inlets R Ranade, T Echekki, AR Masri Flow, Turbulence and Combustion, 1-25, 2022 | 5 | 2022 |
| On the geometry transferability of the hybrid iterative numerical solver for differential equations A Kahana, E Zhang, S Goswami, G Karniadakis, R Ranade, J Pathak Computational Mechanics 72 (3), 471-484, 2023 | 4 | 2023 |
| Geometry encoding for numerical simulations A Maleki, J Heyse, R Ranade, H He, P Kasimbeg, J Pathak arXiv preprint arXiv:2104.07792, 2021 | 4 | 2021 |
| A Latent space solver for PDE generalization R Ranade, C Hill, H He, A Maleki, J Pathak arXiv preprint arXiv:2104.02452, 2021 | 3 | 2021 |
| Large scale scattering using fast solvers based on neural operators Z Zou, A Kahana, E Zhang, E Turkel, R Ranade, J Pathak, GE Karniadakis arXiv preprint arXiv:2405.12380, 2024 | 1 | 2024 |
| Physics-Informed Neural Networks for Turbulent Combustion: Toward Extracting More Statistics and Closure from Point Multiscalar Measurements A Taassob, R Ranade, T Echekki Energy & Fuels 37 (22), 17484-17498, 2023 | 1 | 2023 |
Jay P. PathakSr. Director, Software Development, Ansys Inc在 ansys.com 的电子邮件经过验证
