no code implementations • 7 Nov 2022 • Biswajit Khara, Ethan Herron, Zhanhong Jiang, Aditya Balu, Chih-Hsuan Yang, Kumar Saurabh, Anushrut Jignasu, Soumik Sarkar, Chinmay Hegde, Adarsh Krishnamurthy, Baskar Ganapathysubramanian
Neural network-based approaches for solving partial differential equations (PDEs) have recently received special attention.
no code implementations • 4 Oct 2021 • Biswajit Khara, Aditya Balu, Ameya Joshi, Soumik Sarkar, Chinmay Hegde, Adarsh Krishnamurthy, Baskar Ganapathysubramanian
We consider a mesh-based approach for training a neural network to produce field predictions of solutions to parametric partial differential equations (PDEs).
no code implementations • NeurIPS 2021 • Minsu Cho, Aditya Balu, Ameya Joshi, Anjana Deva Prasad, Biswajit Khara, Soumik Sarkar, Baskar Ganapathysubramanian, Adarsh Krishnamurthy, Chinmay Hegde
Overall, we show that leveraging this redesigned Jacobian in the form of a differentiable "layer" in predictive models leads to improved performance in diverse applications such as image segmentation, 3D point cloud reconstruction, and finite element analysis.
no code implementations • 29 Apr 2021 • Aditya Balu, Sergio Botelho, Biswajit Khara, Vinay Rao, Chinmay Hegde, Soumik Sarkar, Santi Adavani, Adarsh Krishnamurthy, Baskar Ganapathysubramanian
We specifically consider neural solvers for the generalized 3D Poisson equation over megavoxel domains.
no code implementations • 24 Jul 2020 • Sergio Botelho, Ameya Joshi, Biswajit Khara, Soumik Sarkar, Chinmay Hegde, Santi Adavani, Baskar Ganapathysubramanian
Here we report on a software framework for data parallel distributed deep learning that resolves the twin challenges of training these large SciML models - training in reasonable time as well as distributing the storage requirements.