no code implementations • 20 May 2024 • Ramansh Sharma, Varun Shankar
We also present a spatial mixture-of-experts (MoE) DeepONet trunk network architecture that utilizes a partition-of-unity (PoU) approximation to promote spatial locality and model sparsity in the operator learning problem.
no code implementations • 14 Aug 2023 • Mingxuan Han, Varun Shankar, Jeff M Phillips, Chenglong Ye
Over-parameterized models like deep nets and random forests have become very popular in machine learning.
no code implementations • 25 Jul 2023 • Varun Shankar, Romit Maulik, Venkatasubramanian Viswanathan
Differentiable fluid simulators are increasingly demonstrating value as useful tools for developing data-driven models in computational fluid dynamics (CFD).
no code implementations • 7 Jul 2023 • Varun Shankar, Dibyajyoti Chakraborty, Venkatasubramanian Viswanathan, Romit Maulik
Deep learning is increasingly becoming a promising pathway to improving the accuracy of sub-grid scale (SGS) turbulence closure models for large eddy simulations (LES).
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 • 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 • 23 Sep 2022 • Varun Shankar, Vedant Puri, Ramesh Balakrishnan, Romit Maulik, Venkatasubramanian Viswanathan
Data-driven turbulence modeling is experiencing a surge in interest following algorithmic and hardware developments in the data sciences.
1 code implementation • 19 May 2022 • Ramansh Sharma, Varun Shankar
Finally, we also demonstrate that similar results can be obtained for the PINN solution to the heat equation (a space-time problem) by discretizing the spatial derivatives using RBF-FD and using automatic differentiation for the temporal derivative.