A composable machine-learning approach for steady-state simulations on high-resolution grids
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 …
Machine Learning Simulator), can simulate PDEs on highly-resolved grids with higher …
Unsupervised Denoising and Super-Resolution of Vascular Flow Data by Physics-Informed Machine Learning
T Sautory, SC Shadden - Journal of …, 2024 - asmedigitalcollection.asme.org
We present an unsupervised deep learning method to perform flow denoising and super-
resolution without high-resolution labels. We demonstrate the ability of a single model to …
resolution without high-resolution labels. We demonstrate the ability of a single model to …
[PDF][PDF] Supplementary Materials: A composable machine-learning approach for steady-state simulations on high-resolution grids
In the supplementary materials, we provide additional details about our approach and to
support and validate the claims established in the main body of the paper. We have divided …
support and validate the claims established in the main body of the paper. We have divided …
A composable autoencoder-based algorithm for accelerating numerical simulations
Numerical simulations for engineering applications solve partial differential equations (PDE)
to model various physical processes. Traditional PDE solvers are very accurate but …
to model various physical processes. Traditional PDE solvers are very accurate but …