4 citations · 9 across the 4 of their papers we have counts for
4 papers
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…
Preference-based Learning of Reward Function Features
Sydney M. Katz, Amir Maleki, Erdem Bıyık +1
Preference-based learning of reward functions, where the reward function is learned using comparison data, has been well studied for complex robotic tasks such as autonomous drivin…