| 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 | 103 | 2021 |
| 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 | 32 | 2021 |
| An unsupervised learning approach to solving heat equations on chip based on auto encoder and image gradient H He, J Pathak arXiv preprint arXiv:2007.09684, 2020 | 24 | 2020 |
| 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 | 15 | 2022 |
| Solving inverse problems in steady-state navier-stokes equations using deep neural networks T Fan, K Xu, J Pathak, E Darve arXiv preprint arXiv:2008.13074, 2020 | 15 | 2020 |
| 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 | 9 | 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 | 9 | 2021 |
| Physics-consistent deep learning for structural topology optimization J Rade, A Balu, E Herron, J Pathak, R Ranade, S Sarkar, A Krishnamurthy arXiv preprint arXiv:2012.05359, 2020 | 8 | 2020 |
| 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 35, 17386-17401, 2022 | 6 | 2022 |
| 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 |
| 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 | 3 | 2023 |
| Diffusion model based data generation for partial differential equations R Apte, S Nidhan, R Ranade, J Pathak arXiv preprint arXiv:2306.11075, 2023 | 3 | 2023 |
| A composable autoencoder-based iterative algorithm for accelerating numerical simulations R Ranade, C Hill, H He, A Maleki, N Chang, J Pathak arXiv preprint arXiv:2110.03780, 2021 | 3 | 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 |
| Machine-learning based solver of coupled-partial differential equations R Ranade, DC Hill, JP Pathak US Patent App. 16/947,606, 2021 | 1 | 2021 |
| Methods and systems for predicting silicon density for a metal layer of semi-conductor chip via machine learning WT Chuang, N Chang, L Yin, B Zhang, X Chen, JP Pathak, EC Yang, ... US Patent 11,797,744, 2023 | | 2023 |
| Representation learning using machine learning classification tasks based on point clouds of interacting 3D surfaces R Ranade, J Pathak US Patent 11,532,171, 2022 | | 2022 |
| NLP Inspired Training Mechanics For Modeling Transient Dynamics L Ghule, R Ranade, J Pathak arXiv preprint arXiv:2211.02716, 2022 | | 2022 |
| ActivationNet: Representation Learning to Predict Contact Quality of Interacting 3D Surfaces in Engineering Designs R Ranade, J Pathak Journal of Mechanical Design 144 (7), 071705, 2022 | | 2022 |
| A composable autoencoder-based algorithm for accelerating numerical simulations R Ranade, DC Hill, H He, A Maleki, N Chang, J Pathak | | 2021 |
Rishikesh RanadeLead Machine Learning Researcher, Ansys IncVerified email at ansys.com
