31 citations · 54 across the 17 of their papers we have counts for
5 papers · 1 filter
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…
Generalized Joint Probability Density Function Formulation inTurbulent Combustion using DeepONet
Rishikesh Ranade, Kevin Gitushi, Tarek Echekki
Joint probability density function (PDF)-based models in turbulent combustion provide direct closure for turbulence-chemistry interactions. The joint PDFs capture the turbulent fla…
ActivationNet: Representation learning to predict contact quality of interacting 3-D surfaces in engineering designs
Rishikesh Ranade, Jay Pathak
Engineering simulations for analysis of structural and fluid systems require information of contacts between various 3-D surfaces of the geometry to accurately model the physics be…