Investigation of TCADs Models for Characterization of Sub 16 nm In _0.53 Ga _0.47 As FinFET. - Jay Pathak; Anand D. Darji (VDAT, 2017)

Stability Analysis of SRAM Designed Using In0.53Ga0.47As nFinFET with Underlap Region. - Jay Pathak; Anand D. Darji (VLSID, 2019)

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. - Jay Pathak; Anand D. Darji (IET Circuits Devices Syst., 2019)

DiscretizationNet: A Machine-Learning based solver for Navier-Stokes Equations using Finite Volume Discretization. - Rishikesh Ranade; Chris Hill; Jay Pathak (CoRR, 2020)

An unsupervised learning approach to solving heat equations on chip based on Auto Encoder and Image Gradient. - Haiyang He; Jay Pathak (CoRR, 2020)

Solving Inverse Problems in Steady State Navier-Stokes Equations using Deep Neural Networks. - Tiffany Fan; Kailai Xu; Jay Pathak; Eric Darve (CoRR, 2020)

Physics-consistent deep learning for structural topology optimization. - Jaydeep Rade; Aditya Balu; Ethan Herron; Jay Pathak; Rishikesh Ranade; Soumik Sarkar; Adarsh Krishnamurthy (CoRR, 2020)

Analysis of Standard Cells performance for In0.53Ga0.47As FinFET with underlap fin length for High Speed Applications. - Jay Pathak; Anand D. Darji (VDAT, 2021)

ActivationNet: Representation learning to predict contact quality of interacting 3-D surfaces in engineering designs. - Rishikesh Ranade; Jay Pathak (CoRR, 2021)

A Latent space solver for PDE generalization. - Rishikesh Ranade; Chris Hill; Haiyang He; Amir Maleki; Jay Pathak (CoRR, 2021)

One-shot learning for solution operators of partial differential equations. - Lu Lu; Haiyang He; Priya Kasimbeg; Rishikesh Ranade; Jay Pathak (CoRR, 2021)

Geometry encoding for numerical simulations. - Amir Maleki; Jan Heyse; Rishikesh Ranade; Haiyang He; Priya Kasimbeg; Jay Pathak (CoRR, 2021)

A composable autoencoder-based iterative algorithm for accelerating numerical simulations. - Rishikesh Ranade; Chris Hill; Haiyang He; Amir Maleki; Norman Chang; Jay Pathak (CoRR, 2021)

Algorithmically-consistent deep learning frameworks for structural topology optimization. - Jaydeep Rade; Aditya Balu; Ethan Herron; Jay Pathak; Rishikesh Ranade; Soumik Sarkar; Adarsh Krishnamurthy (Eng. Appl. Artif. Intell., 2021)

A Thermal Machine Learning Solver For Chip Simulation. - Rishikesh Ranade; Haiyang He; Jay Pathak; Norman Chang; Akhilesh Kumar; Jimin Wen (MLCAD, 2022)

A composable machine-learning approach for steady-state simulations on high-resolution grids. - Rishikesh Ranade; Chris Hill; Lalit Ghule; Jay Pathak (NeurIPS, 2022)

A Hybrid Iterative Numerical Transferable Solver (HINTS) for PDEs Based on Deep Operator Network and Relaxation Methods. - Enrui Zhang; Adar Kahana; Eli Turkel; Rishikesh Ranade; Jay Pathak; George Em Karniadakis (CoRR, 2022)

On the Geometry Transferability of the Hybrid Iterative Numerical Solver for Differential Equations. - Adar Kahana; Enrui Zhang; Somdatta Goswami; George Em Karniadakis; Rishikesh Ranade; Jay Pathak (CoRR, 2022)

NLP Inspired Training Mechanics For Modeling Transient Dynamics. - Lalit Ghule; Rishikesh Ranade; Jay Pathak (CoRR, 2022)

Diffusion model based data generation for partial differential equations. - Rucha Apte; Sheel Nidhan; Rishikesh Ranade; Jay Pathak (CoRR, 2023)

Large scale scattering using fast solvers based on neural operators. - Zongren Zou; Adar Kahana; Enrui Zhang; Eli Turkel; Rishikesh Ranade; Jay Pathak; George Em Karniadakis (CoRR, 2024)

A domain decomposition-based autoregressive deep learning model for unsteady and nonlinear partial differential equations. - Sheel Nidhan; Haoliang Jiang; Lalit Ghule; Clancy Umphrey; Rishikesh Ranade; Jay Pathak (CoRR, 2024)

Blending neural operators and relaxation methods in PDE numerical solvers. - Enrui Zhang; Adar Kahana; Alena Kopanicáková; Eli Turkel; Rishikesh Ranade; Jay Pathak; George Em Karniadakis (Nat. Mac. Intell., 2024)

Accelerating large-scale linear algebra using variational quantum imaginary time evolution. - Willie Aboumrad; Daiwei Zhu; Claudio Girotto; François-Henry Rouet; Jezer Jojo; Robert F. Lucas; Jay Pathak; Ananth Kaushik; Martin Roetteler (CoRR, 2025)

Algorithmic Advances Towards a Realizable Quantum Lattice Boltzmann Method. - Apurva Tiwari; Jason Iaconis; Jezer Jojo; Sayonee Ray; Martin Roetteler; Chris Hill; Jay Pathak (CoRR, 2025)

CADmium: Fine-Tuning Code Language Models for Text-Driven Sequential CAD Design. - Prashant Govindarajan; Davide Baldelli; Jay Pathak; Quentin Fournier; Sarath Chandar (CoRR, 2025)