5 citations · 12 across the 5 of their papers we have counts for
6 papers
Learning the Shared Latent Structure of Cryo-EM Inverse Problems
Nithin Chalapathi, Theo Sternlieb, Ethan Han +4
Understanding biomolecular function requires characterizing three-dimensional structure and conformational variability, for which cryogenic electron microscopy (cryo-EM) is a power…
PDEInvBench: A Comprehensive Dataset and Design Space Exploration of Neural Networks for PDE Inverse Problems
Divyam Goel, Nithin Chalapathi, Sanjeev Raja +1
Inverse problems in partial differential equations (PDEs) involve estimating the physical parameters of a system from observed spatiotemporal solution fields. Neural networks are w…
Scaling physics-informed hard constraints with mixture-of-experts
Nithin Chalapathi, Yiheng Du, Aditi Krishnapriyan
Imposing known physical constraints, such as conservation laws, during neural network training introduces an inductive bias that can improve accuracy, reliability, convergence, and…
Neural Spectral Methods: Self-supervised learning in the spectral domain
Yiheng Du, Nithin Chalapathi, Aditi Krishnapriyan
We present Neural Spectral Methods, a technique to solve parametric Partial Differential Equations (PDEs), grounded in classical spectral methods. Our method uses orthogonal bases…
Mapper Interactive: A Scalable, Extendable, and Interactive Toolbox for the Visual Exploration of High-Dimensional Data
Youjia Zhou, Nithin Chalapathi, Archit Rathore +2
The mapper algorithm is a popular tool from topological data analysis for extracting topological summaries of high-dimensional datasets. In this paper, we present Mapper Interactiv…
TopoAct: Visually Exploring the Shape of Activations in Deep Learning
Archit Rathore, Nithin Chalapathi, Sourabh Palande +1
Deep neural networks such as GoogLeNet, ResNet, and BERT have achieved impressive performance in tasks such as image and text classification. To understand how such performance is…