20 citations · 34 across the 10 of their papers we have counts for
8 papers · 1 filter
NLP Inspired Training Mechanics For Modeling Transient Dynamics
Lalit Ghule, Rishikesh Ranade, Jay Pathak
In recent years, Machine learning (ML) techniques developed for Natural Language Processing (NLP) have permeated into developing better computer vision algorithms. In this work, we…
A composable machine-learning approach for steady-state simulations on high-resolution grids
Rishikesh Ranade, Chris Hill, Lalit Ghule +1
In this paper we show that our Machine Learning (ML) approach, CoMLSim (Composable Machine Learning Simulator), can simulate PDEs on highly-resolved grids with higher accuracy and…
A Thermal Machine Learning Solver For Chip Simulation
Rishikesh Ranade, Haiyang He, Jay Pathak +3
Thermal analysis provides deeper insights into electronic chips behavior under different temperature scenarios and enables faster design exploration. However, obtaining detailed an…
A composable autoencoder-based iterative algorithm for accelerating numerical simulations
Rishikesh Ranade, Chris Hill, Haiyang He +3
Numerical simulations for engineering applications solve partial differential equations (PDE) to model various physical processes. Traditional PDE solvers are very accurate but com…
Geometry encoding for numerical simulations
Amir Maleki, Jan Heyse, Rishikesh Ranade +3
We present a notion of geometry encoding suitable for machine learning-based numerical simulation. In particular, we delineate how this notion of encoding is different than other e…
A Latent space solver for PDE generalization
Rishikesh Ranade, Chris Hill, Haiyang He +2
In this work we propose a hybrid solver to solve partial differential equation (PDE)s in the latent space. The solver uses an iterative inferencing strategy combined with solution…