5 papers
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 Detonation Flows with Multiscale 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…
Mesh-based Super-Resolution of Fluid Flows with Multiscale Graph Neural Networks
Shivam Barwey, Pinaki Pal, Saumil Patel +5
A graph neural network (GNN) approach is introduced in this work which enables mesh-based three-dimensional super-resolution of fluid flows. In this framework, the GNN is designed…
Understanding Latent Timescales in Neural Ordinary Differential Equation Models for Advection-Dominated Dynamical Systems
Ashish S. Nair, Shivam Barwey, Pinaki Pal +3
The neural ordinary differential equation (ODE) framework has emerged as a powerful tool for developing accelerated surrogate models of complex physical systems governed by partial…
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…