85 citations · 202 across the 44 of their papers we have counts for
8 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…
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…
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…
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…
Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors
Jayaraman J. Thiagarajan, Bindya Venkatesh, Prasanna Sattigeri +1
With rapid adoption of deep learning in critical applications, the question of when and how much to trust these models often arises, which drives the need to quantify the inherent…
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…
A Look at the Effect of Sample Design on Generalization through the Lens of Spectral Analysis
Bhavya Kailkhura, Jayaraman J. Thiagarajan, Qunwei Li +1
This paper provides a general framework to study the effect of sampling properties of training data on the generalization error of the learned machine learning (ML) models. Specifi…