62 citations · 177 across the 45 of their papers we have counts for
10 papers · 1 filter
Improved Surrogates in Inertial Confinement Fusion with Manifold and Cycle Consistencies
Rushil Anirudh, Jayaraman J. Thiagarajan, Peer-Timo Bremer +1
Neural networks have become very popular in surrogate modeling because of their ability to characterize arbitrary, high dimensional functions in a data driven fashion. This paper a…
MimicGAN: Robust Projection onto Image Manifolds with Corruption Mimicking
Rushil Anirudh, Jayaraman J. Thiagarajan, Bhavya Kailkhura +1
In the past few years, Generative Adversarial Networks (GANs) have dramatically advanced our ability to represent and parameterize high-dimensional, non-linear image manifolds. As…
Enabling Machine Learning-Ready HPC Ensembles with Merlin
J. Luc Peterson, Ben Bay, Joe Koning +17
With the growing complexity of computational and experimental facilities, many scientific researchers are turning to machine learning (ML) techniques to analyze large scale ensembl…
Extreme Few-view CT Reconstruction using Deep Inference
Hyojin Kim, Rushil Anirudh, K. Aditya Mohan +1
Reconstruction of few-view x-ray Computed Tomography (CT) data is a highly ill-posed problem. It is often used in applications that require low radiation dose in clinical CT, rapid…
Parallelizing Training of Deep Generative Models on Massive Scientific Datasets
Sam Ade Jacobs, Brian Van Essen, David Hysom +11
Training deep neural networks on large scientific data is a challenging task that requires enormous compute power, especially if no pre-trained models exist to initialize the proce…
Exploring Generative Physics Models with Scientific Priors in Inertial Confinement Fusion
Rushil Anirudh, Jayaraman J. Thiagarajan, Shusen Liu +2
There is significant interest in using modern neural networks for scientific applications due to their effectiveness in modeling highly complex, non-linear problems in a data-drive…
Improving Limited Angle CT Reconstruction with a Robust GAN Prior
Rushil Anirudh, Hyojin Kim, Jayaraman J. Thiagarajan +2
Limited angle CT reconstruction is an under-determined linear inverse problem that requires appropriate regularization techniques to be solved. In this work we study how pre-traine…
Function Preserving Projection for Scalable Exploration of High-Dimensional Data
Shusen Liu, Rushil Anirudh, Jayaraman J. Thiagarajan +1
We present function preserving projections (FPP), a scalable linear projection technique for discovering interpretable relationships in high-dimensional data. Conventional dimensio…
Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications
Shusen Liu, Di Wang, Dan Maljovec +13
With the rapid adoption of machine learning techniques for large-scale applications in science and engineering comes the convergence of two grand challenges in visualization. First…
SALT: Subspace Alignment as an Auxiliary Learning Task for Domain Adaptation
Kowshik Thopalli, Jayaraman J. Thiagarajan, Rushil Anirudh +1
Unsupervised domain adaptation aims to transfer and adapt knowledge learned from a labeled source domain to an unlabeled target domain. Key components of unsupervised domain adapta…