20 citations · 34 across the 7 of their papers we have counts for
6 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…
Active Deep Learning on Entity Resolution by Risk Sampling
Youcef Nafa, Qun Chen, Zhaoqiang Chen +4
While the state-of-the-art performance on entity resolution (ER) has been achieved by deep learning, its effectiveness depends on large quantities of accurately labeled training da…
An unsupervised learning approach to solving heat equations on chip based on Auto Encoder and Image Gradient
Haiyang He, Jay Pathak
Solving heat transfer equations on chip becomes very critical in the upcoming 5G and AI chip-package-systems. However, batches of simulations have to be performed for data driven s…