Obserwuj
Jay P. Pathak
Jay P. Pathak
Sr. Director, Software Development, Ansys Inc
Zweryfikowany adres z ansys.com - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
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
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
452021
Blending Neural Operators and Relaxation Methods in PDE Numerical Solvers
E Zhang, A Kahana, A Kopaničáková, E Turkel, R Ranade, J Pathak, ...
arXiv preprint arXiv:2208.13273, 2022
372022
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
292020
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
192020
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
152021
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
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
92022
Physics-consistent deep learning for structural topology optimization
J Rade, A Balu, E Herron, J Pathak, R Ranade, S Sarkar, A Krishnamurthy
CoRR, 2020
92020
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
82021
Diffusion model based data generation for partial differential equations
R Apte, S Nidhan, R Ranade, J Pathak
arXiv preprint arXiv:2306.11075, 2023
52023
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
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
NLP Inspired Training Mechanics For Modeling Transient Dynamics
L Ghule, R Ranade, J Pathak
arXiv preprint arXiv:2211.02716, 2022
12022
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
12022
A domain decomposition-based autoregressive deep learning model for unsteady and nonlinear partial differential equations
S Nidhan, H Jiang, L Ghule, C Umphrey, R Ranade, J Pathak
arXiv preprint arXiv:2408.14461, 2024
2024
Machine-learning based solver of coupled-partial differential equations
R Ranade, DC Hill, JP Pathak
US Patent App. 18/648,235, 2024
2024
Machine-learning based solver of coupled-partial differential equations
R Ranade, DC Hill, JP Pathak
US Patent 11,995,882, 2024
2024
Nie można teraz wykonać tej operacji. Spróbuj ponownie później.
Prace 1–20