no code implementations • 13 Nov 2023 • Shivam Barwey, 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 of data.
no code implementations • 3 May 2023 • Varun Shankar, Shivam Barwey, Zico Kolter, Romit Maulik, Venkatasubramanian Viswanathan
Graph neural networks (GNNs) have shown promise in learning unstructured mesh-based simulations of physical systems, including fluid dynamics.
no code implementations • 2 May 2023 • Shivam Barwey, Venkat Raman
The goal of this work is to show how the JSK-means algorithm -- without modifying the input dataset -- produces clusters that capture regions of dynamical similarity, in that the clusters are redistributed towards high-sensitivity regions in phase space and are described by similarity in the source terms of samples instead of the samples themselves.
no code implementations • 13 Feb 2023 • Shivam Barwey, Varun Shankar, Venkatasubramanian Viswanathan, Romit Maulik
The goal of this work is to address two limitations in autoencoder-based models: latent space interpretability and compatibility with unstructured meshes.
no code implementations • 22 Sep 2019 • Shivam Barwey, Malik Hassanaly, Venkat Raman, Adam Steinberg
Ultimately, this work shows the powerful ability of the CNN to decode the three-dimensional PIV fields from input OH-PLIF images, providing a potential groundwork for a very useful tool for experimental configurations in which accessibility of forms of simultaneous measurements are limited.