Rishikesh Ranade

According to our database, Rishikesh Ranade authored at least 18 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of three.

2024

Sampling-based Distributed Training with Message Passing Neural Network.

Priyesh Kakka, Sheel Nidhan, Rishikesh Ranade, Jonathan F. MacArt

CoRR, 2024

2023

Diffusion model based data generation for partial differential equations.

Rucha Apte, Sheel Nidhan, Rishikesh Ranade, Jay Pathak

CoRR, 2023

Invited Paper: Solving Fine-Grained Static 3DIC Thermal with ML Thermal Solver Enhanced with Decay Curve Characterization.

Haiyang He, Norman Chang, Jie Yang, Akhilesh Kumar, Wenbo Xia, Lang Lin, Rishikesh Ranade

Proceedings of the IEEE/ACM International Conference on Computer Aided Design, 2023

2022

NLP Inspired Training Mechanics For Modeling Transient Dynamics.

Lalit Ghule, Rishikesh Ranade, Jay Pathak

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

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

A composable machine-learning approach for steady-state simulations on high-resolution grids.

Rishikesh Ranade, Chris Hill, Lalit Ghule, Jay Pathak

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

A Thermal Machine Learning Solver For Chip Simulation.

Rishikesh Ranade, Haiyang He, Jay Pathak, Norman Chang, Akhilesh Kumar, Jimin Wen

Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD, 2022

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 composable autoencoder-based iterative algorithm for accelerating numerical simulations.

Rishikesh Ranade, Chris Hill, Haiyang He, Amir Maleki, Norman Chang, Jay Pathak

CoRR, 2021

Geometry encoding for numerical simulations.

Amir Maleki, Jan Heyse, Rishikesh Ranade, Haiyang He, Priya Kasimbeg, 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

A Latent space solver for PDE generalization.

Rishikesh Ranade, Chris Hill, Haiyang He, Amir Maleki, Jay Pathak

CoRR, 2021

Generalized Joint Probability Density Function Formulation inTurbulent Combustion using DeepONet.

Rishikesh Ranade, Kevin Gitushi, Tarek Echekki

CoRR, 2021

ActivationNet: Representation learning to predict contact quality of interacting 3-D surfaces in engineering designs.

Rishikesh Ranade, Jay Pathak

CoRR, 2021

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

An Efficient Machine-Learning Approach for PDF Tabulation in Turbulent Combustion Closure.

Rishikesh Ranade, Genong Li, Shaoping Li, Tarek Echekki

CoRR, 2020

DiscretizationNet: A Machine-Learning based solver for Navier-Stokes Equations using Finite Volume Discretization.

Rishikesh Ranade, Chris Hill, Jay Pathak

CoRR, 2020