no code implementations • 15 Feb 2024 • Muhammad Arbab Arshad, Talukder Jubery, James Afful, Anushrut Jignasu, Aditya Balu, Baskar Ganapathysubramanian, Soumik Sarkar, Adarsh Krishnamurthy
We evaluate different Neural Radiance Fields (NeRFs) techniques for reconstructing (3D) plants in varied environments, from indoor settings to outdoor fields.
no code implementations • 20 Sep 2023 • Ethan Herron, Jaydeep Rade, Anushrut Jignasu, Baskar Ganapathysubramanian, Aditya Balu, Soumik Sarkar, Adarsh Krishnamurthy
Specifically, we employ a Latent Diffusion model to generate potential designs of a component that can satisfy a set of problem-specific loading conditions.
1 code implementation • 4 Sep 2023 • Anushrut Jignasu, Kelly Marshall, Baskar Ganapathysubramanian, Aditya Balu, Chinmay Hegde, Adarsh Krishnamurthy
3D printing or additive manufacturing is a revolutionary technology that enables the creation of physical objects from digital models.
1 code implementation • 14 Jun 2023 • Kelly O. Marshall, Minh Pham, Ameya Joshi, Anushrut Jignasu, Aditya Balu, Adarsh Krishnamurthy, Chinmay Hegde
Current state-of-the-art methods for text-to-shape generation either require supervised training using a labeled dataset of pre-defined 3D shapes, or perform expensive inference-time optimization of implicit neural representations.
no code implementations • 20 Feb 2023 • Stefan Druc, Peter Wooldridge, Adarsh Krishnamurthy, Soumik Sarkar, Aditya Balu
Deep learning is becoming increasingly adopted in business and industry due to its ability to transform large quantities of data into high-performing models.
no code implementations • 26 Nov 2022 • Jaydeep Rade, Soumik Sarkar, Anwesha Sarkar, Adarsh Krishnamurthy
These multi-view images can help train the neural network to predict the 3D structure of protein complexes.
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 • 29 Apr 2022 • Stefan Druc, Aditya Balu, Peter Wooldridge, Adarsh Krishnamurthy, Soumik Sarkar
We explore the interpretability of 3D geometric deep learning models in the context of Computer-Aided Design (CAD).
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 • 29 Apr 2021 • Anjana Deva Prasad, Aditya Balu, Harshil Shah, Soumik Sarkar, Chinmay Hegde, Adarsh Krishnamurthy
These derivatives are used to define an approximate Jacobian used for performing the "backward" evaluation to train the deep learning models.
no code implementations • NeurIPS Workshop LMCA 2020 • Minsu Cho, Ameya Joshi, Xian Yeow Lee, Aditya Balu, Adarsh Krishnamurthy, Baskar Ganapathysubramanian, Soumik Sarkar, Chinmay Hegde
The paradigm of differentiable programming has considerably enhanced the scope of machine learning via the judicious use of gradient-based optimization.
no code implementations • 9 Dec 2020 • Jaydeep Rade, Aditya Balu, Ethan Herron, Jay Pathak, Rishikesh Ranade, Soumik Sarkar, Adarsh Krishnamurthy
We achieve this by training multiple networks, each learning a different step of the overall topology optimization methodology, making the framework more consistent with the topology optimization algorithm.
1 code implementation • 30 May 2018 • Sambit Ghadai, Xian Lee, Aditya Balu, Soumik Sarkar, Adarsh Krishnamurthy
The multi-level voxel representation consists of a coarse voxel grid that contains volumetric information of the 3D object.
1 code implementation • 13 Nov 2017 • Sambit Ghadai, Aditya Balu, Adarsh Krishnamurthy, Soumik Sarkar
3D Convolutional Neural Networks (3D-CNN) have been used for object recognition based on the voxelized shape of an object.
no code implementations • 4 Mar 2017 • Aditya Balu, Sambit Ghadai, Gavin Young, Soumik Sarkar, Adarsh Krishnamurthy
this is a duplicate submission(original is arXiv:1612. 02141).
no code implementations • 7 Dec 2016 • Aditya Balu, Sambit Ghadai, Kin Gwn Lore, Gavin Young, Adarsh Krishnamurthy, Soumik Sarkar
3D convolutional neural networks (3D-CNN) have been used for object recognition based on the voxelized shape of an object.