43 citations · 86 across the 18 of their papers we have counts for
4 papers · 1 filter
Mesh-based Super-resolution of Multiscale Detonation Flows with Graph Transformers
Shivam Barwey, Pinaki Pal
Super-resolution flow reconstruction using state-of-the-art data-driven techniques is valuable for a variety of applications, such as subgrid/subfilter closure modeling, accelerati…
FIGNN: Feature-Specific Interpretability for Graph Neural Network Surrogate Models
Riddhiman Raut, Romit Maulik, Shivam Barwey
This work presents a novel graph neural network (GNN) architecture, the Feature-specific Interpretable Graph Neural Network (FIGNN), designed to enhance the interpretability of dee…
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…
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…