Search Results for author:
Found 6 papers, 3 papers with code
Super-Resolution Analysis via Machine Learning: A Survey for Fluid Flows
This paper surveys machine-learning-based super-resolution reconstruction for vortical flows.
Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regression
The average of such an ensemble can be regarded as the `mean estimation', whereas its standard deviation can be used to construct `confidence intervals', which enable us to perform uncertainty quantification (UQ) with regard to the training process of neural networks.
Convolutional neural networks for fluid flow analysis: toward effective metamodeling and low-dimensionalization
We then discuss the influence of various parameters and operations on the CNN performance, with the utilization of autoencoder (AE).
Dimensionality Reduction
Fluid Dynamics
Computational Physics
Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning
This reconstruction problem is especially difficult when sensors are sparsely positioned in a seemingly random or unorganized manner, which is often encountered in a range of scientific and engineering problems.
Probabilistic neural networks for fluid flow surrogate modeling and data recovery
We consider the use of probabilistic neural networks for fluid flow surrogate modeling and data recovery.
Fluid Dynamics
Machine-learning-based reduced order modeling for unsteady flows around bluff bodies of various shapes
The present ML-ROMs are trained on a set of 80 bluff body shapes and tested on a different set of 20 bluff body shapes not used for training, with both training and test shapes chosen from the same random distribution.
Fluid Dynamics Computational Physics
