85 citations · 85 across the 3 of their papers we have counts for
6 papers
A joint diffusion approach to multi-modal inference in inertial confinement fusion
Michael S. Jones, Justin Kunimune, Daniel Casey +3
A combination of physics-based simulation and experiments has been critical to achieving ignition in inertial confinement fusion (ICF). Simulation and experiment both produce a mix…
Transformer-Powered Surrogates Close the ICF Simulation-Experiment Gap with Extremely Limited Data
Matthew L. Olson, Shusen Liu, Jayaraman J. Thiagarajan +3
Recent advances in machine learning, specifically transformer architecture, have led to significant advancements in commercial domains. These powerful models have demonstrated supe…
2022 Review of Data-Driven Plasma Science
Rushil Anirudh, Rick Archibald, M. Salman Asif +60
Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data…
The data-driven future of high energy density physics
Peter W. Hatfield, Jim A. Gaffney, Gemma J. Anderson +20
The study of plasma physics under conditions of extreme temperatures, densities and electromagnetic field strengths is significant for our understanding of astrophysics, nuclear fu…
Suppressing simulation bias using multi-modal data
Bogdan Kustowski, Jim A. Gaffney, Brian K. Spears +6
Many problems in science and engineering require making predictions based on few observations. To build a robust predictive model, these sparse data may need to be augmented with s…
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…