13 citations · 30 across the 9 of their papers we have counts for
9 papers
HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs
Jinpai Zhao, Nishant Panda, Yen Ting Lin +3
We introduce HyCOP, a modular framework that learns parametric PDE solution operators by composing simple modules (advection, diffusion, learned closures, boundary handling) in a q…
Semi-supervised Learning of Pushforwards For Domain Translation & Adaptation
Nishant Panda, Natalie Klein, Dominic Yang +2
Given two probability densities on related data spaces, we seek a map pushing one density to the other while satisfying application-dependent constraints. For maps to have utility…
Generative structured normalizing flow Gaussian processes applied to spectroscopic data
Natalie Klein, Nishant Panda, Patrick Gasda +1
In this work, we propose a novel generative model for mapping inputs to structured, high-dimensional outputs using structured conditional normalizing flows and Gaussian process reg…
BB-ML: Basic Block Performance Prediction using Machine Learning Techniques
Hamdy Abdelkhalik, Shamminuj Aktar, Yehia Arafa +8
Recent years have seen the adoption of Machine Learning (ML) techniques to predict the performance of large-scale applications, mostly at a coarse level. In contrast, we propose to…
Neural density estimation and uncertainty quantification for laser induced breakdown spectroscopy spectra
Katiana Kontolati, Natalie Klein, Nishant Panda +1
Constructing probability densities for inference in high-dimensional spectral data is often intractable. In this work, we use normalizing flows on structured spectral latent spaces…
StressNet: Deep Learning to Predict Stress With Fracture Propagation in Brittle Materials
Yinan Wang, Diane Oyen, Weihong +7
Catastrophic failure in brittle materials is often due to the rapid growth and coalescence of cracks aided by high internal stresses. Hence, accurate prediction of maximum internal…
Accelerating High-Strain Continuum-Scale Brittle Fracture Simulations with Machine Learning
M. Giselle Fernández-Godino, Nishant Panda, Daniel O'Malley +4
Failure in brittle materials under dynamic loading conditions is a result of the propagation and coalescence of microcracks. Simulating this mechanism at the continuum level is com…
What is the gradient of a scalar function of a symmetric matrix ?
Shriram Srinivasan, Nishant Panda
Perusal of research articles that deal with the topic of matrix calculus reveal two different approaches to calculation of the gradient of a real-valued function of a symmetric mat…
Estimating Failure in Brittle Materials using Graph Theory
M. K. Mudunuru, N. Panda, S. Karra +5
In brittle fracture applications, failure paths, regions where the failure occurs and damage statistics, are some of the key quantities of interest (QoI). High-fidelity models for…