Using machine learning to construct velocity fields from OH-PLIF images. - Shivam Barwey; Malik Hassanaly; Venkat Raman; Adam Steinberg (CoRR, 2019)

Importance of equivariant and invariant symmetries for fluid flow modeling. - Varun Shankar; Shivam Barwey; Zico Kolter; Romit Maulik; Venkatasubramanian Viswanathan (CoRR, 2023)

Interpretable Fine-Tuning for Graph Neural Network Surrogate Models. - Shivam Barwey; Romit Maulik (CoRR, 2023)

Multiscale graph neural network autoencoders for interpretable scientific machine learning. - Shivam Barwey; Varun Shankar; Venkatasubramanian Viswanathan; Romit Maulik (J. Comput. Phys., 2023)

Scalable and Consistent Graph Neural Networks for Distributed Mesh-based Data-driven Modeling. - Shivam Barwey; Riccardo Balin; Bethany Lusch; Saumil Patel; Ramesh Balakrishnan; Pinaki Pal; Romit Maulik; Venkatram Vishwanath (SC Workshops, 2024)

A note on the error analysis of data-driven closure models for large eddy simulations of turbulence. - Dibyajyoti Chakraborty; Shivam Barwey; Hong Zhang; Romit Maulik (CoRR, 2024)

Mesh-based Super-Resolution of Fluid Flows with Multiscale Graph Neural Networks. - Shivam Barwey; Pinaki Pal; Saumil Patel; Riccardo Balin; Bethany Lusch; Venkatram Vishwanath; Romit Maulik; Ramesh Balakrishnan (CoRR, 2024)

Jacobian-scaled K-means clustering for physics-informed segmentation of reacting flows. - Shivam Barwey; Venkat Raman (J. Comput. Phys., 2024)

FIGNN: Feature-Specific Interpretability for Graph Neural Network Surrogate Models. - Riddhiman Raut; Romit Maulik; Shivam Barwey (CoRR, 2025)