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Found 5 papers, 0 papers with code
Interpretable Fine-Tuning for Graph Neural Network Surrogate Models
Data-driven surrogate modeling has surged in capability in recent years with the emergence of graph neural networks (GNNs), which can operate directly on mesh-based representations of data.
Importance of equivariant and invariant symmetries for fluid flow modeling
Graph neural networks (GNNs) have shown promise in learning unstructured mesh-based simulations of physical systems, including fluid dynamics.
Jacobian-Scaled K-means Clustering for Physics-Informed Segmentation of Reacting Flows
The goal of this work is to show how the JSK-means algorithm -- without modifying the input dataset -- produces clusters that capture regions of dynamical similarity, in that the clusters are redistributed towards high-sensitivity regions in phase space and are described by similarity in the source terms of samples instead of the samples themselves.
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.
Using machine learning to construct velocity fields from OH-PLIF images
Ultimately, this work shows the powerful ability of the CNN to decode the three-dimensional PIV fields from input OH-PLIF images, providing a potential groundwork for a very useful tool for experimental configurations in which accessibility of forms of simultaneous measurements are limited.
