60 citations · 121 across the 21 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…
One-shot learning for solution operators of partial differential equations
Anran Jiao, Haiyang He, Rishikesh Ranade +2
Learning and solving governing equations of a physical system, represented by partial differential equations (PDEs), from data is a central challenge in a variety of areas of scien…
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…