6 citations · 10 across the 4 of their papers we have counts for
4 papers
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…
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…
Transfer learning to model inertial confinement fusion experiments
K. D. Humbird, J. L. Peterson, R. G. McClarren
Inertial confinement fusion (ICF) experiments are designed using computer simulations that are approximations of reality, and therefore must be calibrated to accurately predict exp…