Super-resolution analysis via machine learning: a survey for fluid flows
This paper surveys machine-learning-based super-resolution reconstruction for vortical
flows. Super resolution aims to find the high-resolution flow fields from low-resolution data …
flows. Super resolution aims to find the high-resolution flow fields from low-resolution data …
Multiscale graph neural network autoencoders for interpretable scientific machine learning
The goal of this work is to address two limitations in autoencoder-based models: latent
space interpretability and compatibility with unstructured meshes. This is accomplished here …
space interpretability and compatibility with unstructured meshes. This is accomplished here …
High-resolution meteorology with climate change impacts from global climate model data using generative machine learning
As renewable energy generation increases, the impacts of weather and climate on energy
generation and demand become critical to the reliability of the energy system. However …
generation and demand become critical to the reliability of the energy system. However …
Investigation of the generalization capability of a generative adversarial network for large eddy simulation of turbulent premixed reacting flows
In the past decades, Deep Learning (DL) frameworks have demonstrated excellent
performance in modeling nonlinear interactions and are a promising technique to move …
performance in modeling nonlinear interactions and are a promising technique to move …
PINN surrogate of Li-ion battery models for parameter inference, Part II: Regularization and application of the pseudo-2D model
Bayesian parameter inference is useful to improve Li-ion battery diagnostics and can help
formulate battery aging models. However, it is computationally intensive and cannot be …
formulate battery aging models. However, it is computationally intensive and cannot be …
[HTML][HTML] Ensemble flow reconstruction in the atmospheric boundary layer from spatially limited measurements through latent diffusion models
Due to costs and practical constraints, field campaigns in the atmospheric boundary layer
typically only measure a fraction of the atmospheric volume of interest. Machine learning …
typically only measure a fraction of the atmospheric volume of interest. Machine learning …
High-resolution reconstruction and a-priori modeling of turbulent flames in the context of large eddy simulation using the convolutional neural network
In large eddy simulation (LES) of turbulent combustion, accurate modeling of the unresolved
scalar flux and filtered reaction source terms is challenging. In the present work, a …
scalar flux and filtered reaction source terms is challenging. In the present work, a …
Conditional sampling with monotone GANs: from generative models to likelihood-free inference
We present a novel framework for conditional sampling of probability measures, using block
triangular transport maps. We develop the theoretical foundations of block triangular …
triangular transport maps. We develop the theoretical foundations of block triangular …
Influence of adversarial training on super-resolution turbulence reconstruction
Supervised super-resolution deep convolutional neural networks (CNNs) have gained
significant attention for their potential in reconstructing velocity and scalar fields in turbulent …
significant attention for their potential in reconstructing velocity and scalar fields in turbulent …
Jacobian-scaled K-means clustering for physics-informed segmentation of reacting flows
This work introduces Jacobian-scaled K-means (JSK-means) clustering, which is a physics-
informed clustering strategy centered on the K-means framework. The method allows for the …
informed clustering strategy centered on the K-means framework. The method allows for the …