43 citations · 86 across the 18 of their papers we have counts for
4 papers · 1 filter
Interpretable A-posteriori Error Indication for Graph Neural Network Surrogate Models
Shivam Barwey, Hojin Kim, Romit Maulik
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…
Importance of equivariant and invariant symmetries for fluid flow modeling
Varun Shankar, Shivam Barwey, Zico Kolter +2
Graph neural networks (GNNs) have shown promise in learning unstructured mesh-based simulations of physical systems, including fluid dynamics. In tandem, geometric deep learning pr…
Jacobian-Scaled K-means Clustering for Physics-Informed Segmentation of Reacting Flows
Shivam Barwey, Venkat Raman
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 in…
Multiscale Graph Neural Network Autoencoders for Interpretable Scientific Machine Learning
Shivam Barwey, Varun Shankar, Venkatasubramanian Viswanathan +1
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…