4 citations · 8 across the 4 of their papers we have counts for
3 papers · 1 filter
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 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…
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…