43 citations · 86 across the 18 of their papers we have counts for
19 papers
Generating synthetic evolution of turbulent flames with an experimental data-based spatiotemporal diffusion model
Amrit Tarur, Shivam Barwey
In this study, a conditional diffusion model -- a class of generative machine learning models -- is developed to generate synthetic, experimental data-based trajectories of turbule…
A Multimodal Vision Transformer-based Modeling Framework for Prediction of Fluid Flows in Energy Systems
Kiran Yalamanchi, Shivam Barwey, Ibrahim Jarrah +1
Computational fluid dynamics (CFD) simulations of complex fluid flows in energy systems are prohibitively expensive due to strong nonlinearities and multiscale-multiphysics interac…
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…
An AMReX-based Compressible Reacting Flow Solver for High-speed Reacting Flows relevant to Hypersonic Propulsion
Shivank Sharma, Ral Bielawski, Oliver Gibson +8
This work presents a comprehensive framework for the efficient implementation of finite-volume-based reacting flow solvers, specifically tailored for high speed propulsion applicat…
Scalable and Consistent Graph Neural Networks for Distributed Mesh-based Data-driven Modeling
Shivam Barwey, Riccardo Balin, Bethany Lusch +5
This work develops a distributed graph neural network (GNN) methodology for mesh-based modeling applications using a consistent neural message passing layer. As the name implies, t…