Jay Pathak - RankMe

31 papers A* 1Misc 1Journal 26Unranked 3

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	CADmium: Fine-Tuning Code Language Models for Text- Driven Sequential CAD Design. Trans. Mach. Learn. Res. Prashant Govindarajan, Davide Baldelli, Jay Pathak, Quentin Fournier, Sarath Chandar
2025	J	`jnl`	Accelerating large-scale linear algebra using variational quantum imaginary time evolution. CoRR Willie Aboumrad, Daiwei Zhu, Claudio Girotto, François-Henry Rouet, Jezer Jojo, Robert F. Lucas, Jay Pathak, Ananth Kaushik, Martin Roetteler
2025	J	`jnl`	Algorithmic Advances Towards a Realizable Quantum Lattice Boltzmann Method. CoRR Apurva Tiwari, Jason Iaconis, Jezer Jojo, Sayonee Ray, Martin Roetteler, Chris Hill, Jay Pathak
2025	J	`jnl`	CADmium: Fine-Tuning Code Language Models for Text-Driven Sequential CAD Design. CoRR Prashant Govindarajan, Davide Baldelli, Jay Pathak, Quentin Fournier, Sarath Chandar
2025	J	`jnl`	Hamiltonian Simulation for Advection-Diffusion Equation with arbitrary transport field. CoRR Niladri Gomes, Gautam Sharma, Jay Pathak
2025	J	`jnl`	Hybrid Iterative Solvers with Geometry-Aware Neural Preconditioners for Parametric PDEs. CoRR Youngkyu Lee, Francesc Levrero-Florencio, Jay Pathak, George Em Karniadakis
2024	J	`jnl`	A domain decomposition-based autoregressive deep learning model for unsteady and nonlinear partial differential equations. CoRR Sheel Nidhan, Haoliang Jiang, Lalit Ghule, Clancy Umphrey, Rishikesh Ranade, Jay Pathak
2024	J	`jnl`	Blending neural operators and relaxation methods in PDE numerical solvers. Nat. Mac. Intell. Enrui Zhang, Adar Kahana, Alena Kopanicáková, Eli Turkel, Rishikesh Ranade, Jay Pathak, George Em Karniadakis
2024	J	`jnl`	Large scale scattering using fast solvers based on neural operators. CoRR Zongren Zou, Adar Kahana, Enrui Zhang, Eli Turkel, Rishikesh Ranade, Jay Pathak, George Em Karniadakis
2023	J	`jnl`	Diffusion model based data generation for partial differential equations. CoRR Rucha Apte, Sheel Nidhan, Rishikesh Ranade, Jay Pathak
2022	J	`jnl`	A Hybrid Iterative Numerical Transferable Solver (HINTS) for PDEs Based on Deep Operator Network and Relaxation Methods. CoRR Enrui Zhang, Adar Kahana, Eli Turkel, Rishikesh Ranade, Jay Pathak, George Em Karniadakis
2022	—	`conf`	A Thermal Machine Learning Solver For Chip Simulation. MLCAD Rishikesh Ranade, Haiyang He, Jay Pathak, Norman Chang, Akhilesh Kumar, Jimin Wen
2022	J	`jnl`	A Thermal Machine Learning Solver For Chip Simulation. CoRR Rishikesh Ranade, Haiyang He, Jay Pathak, Norman Chang, Akhilesh Kumar, Jimin Wen
2022	A*	`conf`	A composable machine-learning approach for steady-state simulations on high-resolution grids. NeurIPS Rishikesh Ranade, Chris Hill, Lalit Ghule, Jay Pathak
2022	J	`jnl`	A composable machine-learning approach for steady-state simulations on high-resolution grids. CoRR Rishikesh Ranade, Chris Hill, Lalit Ghule, Jay Pathak
2022	J	`jnl`	NLP Inspired Training Mechanics For Modeling Transient Dynamics. CoRR Lalit Ghule, Rishikesh Ranade, Jay Pathak
2022	J	`jnl`	On the Geometry Transferability of the Hybrid Iterative Numerical Solver for Differential Equations. CoRR Adar Kahana, Enrui Zhang, Somdatta Goswami, George Em Karniadakis, Rishikesh Ranade, Jay Pathak
2021	J	`jnl`	A Latent space solver for PDE generalization. CoRR Rishikesh Ranade, Chris Hill, Haiyang He, Amir Maleki, Jay Pathak
2021	J	`jnl`	A composable autoencoder-based iterative algorithm for accelerating numerical simulations. CoRR Rishikesh Ranade, Chris Hill, Haiyang He, Amir Maleki, Norman Chang, Jay Pathak
2021	J	`jnl`	ActivationNet: Representation learning to predict contact quality of interacting 3-D surfaces in engineering designs. CoRR Rishikesh Ranade, Jay Pathak
2021	J	`jnl`	Algorithmically-consistent deep learning frameworks for structural topology optimization. Eng. Appl. Artif. Intell. Jaydeep Rade, Aditya Balu, Ethan Herron, Jay Pathak, Rishikesh Ranade, Soumik Sarkar, Adarsh Krishnamurthy
2021	—	`conf`	Analysis of Standard Cells performance for In0.53Ga0.47As FinFET with underlap fin length for High Speed Applications. VDAT Jay Pathak, Anand D. Darji
2021	J	`jnl`	Geometry encoding for numerical simulations. CoRR Amir Maleki, Jan Heyse, Rishikesh Ranade, Haiyang He, Priya Kasimbeg, Jay Pathak
2021	J	`jnl`	One-shot learning for solution operators of partial differential equations. CoRR Lu Lu, Haiyang He, Priya Kasimbeg, Rishikesh Ranade, Jay Pathak
2020	J	`jnl`	An unsupervised learning approach to solving heat equations on chip based on Auto Encoder and Image Gradient. CoRR Haiyang He, Jay Pathak
2020	J	`jnl`	DiscretizationNet: A Machine-Learning based solver for Navier-Stokes Equations using Finite Volume Discretization. CoRR Rishikesh Ranade, Chris Hill, Jay Pathak
2020	J	`jnl`	Physics-consistent deep learning for structural topology optimization. CoRR Jaydeep Rade, Aditya Balu, Ethan Herron, Jay Pathak, Rishikesh Ranade, Soumik Sarkar, Adarsh Krishnamurthy
2020	J	`jnl`	Solving Inverse Problems in Steady State Navier-Stokes Equations using Deep Neural Networks. CoRR Tiffany Fan, Kailai Xu, Jay Pathak, Eric Darve
2019	J	`jnl`	Assessment of interface traps in In IET Circuits Devices Syst. Jay Pathak, Anand D. Darji
2019	Misc	`conf`	Stability Analysis of SRAM Designed Using In0.53Ga0.47As nFinFET with Underlap Region. VLSID Jay Pathak, Anand D. Darji
2017	—	`conf`	Investigation of TCADs Models for Characterization of Sub 16 nm In _0.53 Ga _0.47 As FinFET. VDAT Jay Pathak, Anand D. Darji