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Rishikesh Ranade
Rishikesh Ranade
Senior Engineer - Physics ML, NVIDIA
Verified email at nvidia.com
Title
Cited by
Cited by
Year
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
1422021
An ANN based hybrid chemistry framework for complex fuels
R Ranade, S Alqahtani, A Farooq, T Echekki
Fuel 241, 625-636, 2019
632019
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
57*2021
A framework for data-based turbulent combustion closure: A posteriori validation
R Ranade, T Echekki
Combustion and flame 210, 279-291, 2019
492019
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, 198, 2022
39*2022
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
362021
A framework for data-based turbulent combustion closure: A priori validation
R Ranade, T Echekki
Combustion and Flame 206, 490-505, 2019
362019
An extended hybrid chemistry framework for complex hydrocarbon fuels
R Ranade, S Alqahtani, A Farooq, T Echekki
Fuel 251, 276-284, 2019
282019
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
262022
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
172022
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
162021
Generalized joint probability density function formulation inturbulent combustion using deeponet
R Ranade, K Gitushi, T Echekki
arXiv preprint arXiv:2104.01996, 2021
162021
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
15*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
142023
Diffusion model based data generation for partial differential equations
R Apte, S Nidhan, R Ranade, J Pathak
arXiv preprint arXiv:2306.11075, 2023
52023
Experiment-based modeling of turbulent flames with inhomogeneous inlets
R Ranade, T Echekki, AR Masri
Flow, Turbulence and Combustion, 1-25, 2022
52022
A latent space solver for PDE generalization
R Ranade, C Hill, H He, A Maleki, J Pathak
arXiv preprint arXiv:2104.02452, 2021
52021
Geometry encoding for numerical simulations
A Maleki, J Heyse, R Ranade, H He, P Kasimbeg, J Pathak
arXiv preprint arXiv:2104.07792, 2021
42021
A PINN-DeepONet framework for extracting turbulent combustion closure from multiscalar measurements
A Taassob, A Kumar, KM Gitushi, R Ranade, T Echekki
Computer Methods in Applied Mechanics and Engineering 429, 117163, 2024
32024
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
32024
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