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Journal Articles
- 2021
[j2]Jaydeep Rade
, Aditya Balu, Ethan Herron, Jay Pathak, Rishikesh Ranade, Soumik Sarkar, Adarsh Krishnamurthy:
Algorithmically-consistent deep learning frameworks for structural topology optimization. Eng. Appl. Artif. Intell. 106: 104483 (2021)- 2019
- [j1]Jay Pathak, Anand D. Darji:
Assessment of interface traps in In0.53Ga0.47As FinFET with gate-to-source/drain underlap for sub-14 nm technology node to impede short channel effect. IET Circuits Devices Syst. 13(4): 428-434 (2019)
Conference and Workshop Papers
- 2022
- [c5]Rishikesh Ranade, Haiyang He, Jay Pathak, Norman Chang, Akhilesh Kumar, Jimin Wen:
A Thermal Machine Learning Solver For Chip Simulation. MLCAD 2022: 111-117 - [c4]Rishikesh Ranade, Chris Hill, Lalit Ghule, Jay Pathak:
A composable machine-learning approach for steady-state simulations on high-resolution grids. NeurIPS 2022 - 2021
- [c3]Jay Pathak, Anand D. Darji:
Analysis of Standard Cells performance for In0.53Ga0.47As FinFET with underlap fin length for High Speed Applications. VDAT 2021: 1-4 - 2019
- [c2]Jay Pathak, Anand D. Darji:
Stability Analysis of SRAM Designed Using In0.53Ga0.47As nFinFET with Underlap Region. VLSID 2019: 502-503 - 2017
- [c1]Jay Pathak, Anand D. Darji:
Investigation of TCADs Models for Characterization of Sub 16 nm In _0.53 Ga _0.47 As FinFET. VDAT 2017: 279-286
Informal and Other Publications
- 2024
- [i17]Zongren Zou, Adar Kahana, Enrui Zhang, Eli Turkel, Rishikesh Ranade, Jay Pathak, George Em Karniadakis:
Large scale scattering using fast solvers based on neural operators. CoRR abs/2405.12380 (2024) - [i16]Sheel Nidhan, Haoliang Jiang, Lalit Ghule, Clancy Umphrey, Rishikesh Ranade, Jay Pathak:
A domain decomposition-based autoregressive deep learning model for unsteady and nonlinear partial differential equations. CoRR abs/2408.14461 (2024) - 2023
- [i15]Rucha Apte, Sheel Nidhan, Rishikesh Ranade, Jay Pathak:
Diffusion model based data generation for partial differential equations. CoRR abs/2306.11075 (2023) - 2022
- [i14]Enrui Zhang, Adar Kahana, Eli Turkel, Rishikesh Ranade, Jay Pathak, George Em Karniadakis:
A Hybrid Iterative Numerical Transferable Solver (HINTS) for PDEs Based on Deep Operator Network and Relaxation Methods. CoRR abs/2208.13273 (2022) - [i13]Rishikesh Ranade, Haiyang He, Jay Pathak, Norman Chang, Akhilesh Kumar, Jimin Wen:
A Thermal Machine Learning Solver For Chip Simulation. CoRR abs/2209.04741 (2022) - [i12]Rishikesh Ranade, Chris Hill, Lalit Ghule, Jay Pathak:
A composable machine-learning approach for steady-state simulations on high-resolution grids. CoRR abs/2210.05837 (2022) - [i11]Adar Kahana, Enrui Zhang, Somdatta Goswami, George Em Karniadakis, Rishikesh Ranade, Jay Pathak:
On the Geometry Transferability of the Hybrid Iterative Numerical Solver for Differential Equations. CoRR abs/2210.17392 (2022) - [i10]Lalit Ghule, Rishikesh Ranade, Jay Pathak:
NLP Inspired Training Mechanics For Modeling Transient Dynamics. CoRR abs/2211.02716 (2022) - 2021
- [i9]Rishikesh Ranade, Jay Pathak:
ActivationNet: Representation learning to predict contact quality of interacting 3-D surfaces in engineering designs. CoRR abs/2103.11288 (2021) - [i8]Rishikesh Ranade, Chris Hill, Haiyang He, Amir Maleki, Jay Pathak:
A Latent space solver for PDE generalization. CoRR abs/2104.02452 (2021) - [i7]Lu Lu, Haiyang He, Priya Kasimbeg, Rishikesh Ranade, Jay Pathak:
One-shot learning for solution operators of partial differential equations. CoRR abs/2104.05512 (2021) - [i6]Amir Maleki, Jan Heyse, Rishikesh Ranade, Haiyang He, Priya Kasimbeg, Jay Pathak:
Geometry encoding for numerical simulations. CoRR abs/2104.07792 (2021) - [i5]Rishikesh Ranade, Chris Hill, Haiyang He, Amir Maleki, Norman Chang, Jay Pathak:
A composable autoencoder-based iterative algorithm for accelerating numerical simulations. CoRR abs/2110.03780 (2021) - 2020
- [i4]Rishikesh Ranade, Chris Hill, Jay Pathak:
DiscretizationNet: A Machine-Learning based solver for Navier-Stokes Equations using Finite Volume Discretization. CoRR abs/2005.08357 (2020) - [i3]Haiyang He, Jay Pathak:
An unsupervised learning approach to solving heat equations on chip based on Auto Encoder and Image Gradient. CoRR abs/2007.09684 (2020) - [i2]Tiffany Fan, Kailai Xu, Jay Pathak, Eric Darve:
Solving Inverse Problems in Steady State Navier-Stokes Equations using Deep Neural Networks. CoRR abs/2008.13074 (2020) - [i1]Jaydeep Rade, Aditya Balu, Ethan Herron, Jay Pathak, Rishikesh Ranade, Soumik Sarkar, Adarsh Krishnamurthy:
Physics-consistent deep learning for structural topology optimization. CoRR abs/2012.05359 (2020)
Coauthor Index
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