Transformer-Powered Surrogates Close the ICF Simulation-Experiment Gap with Extremely Limited Data

no code implementations • 6 Dec 2023 • Matthew L. Olson, Shusen Liu, Jayaraman J. Thiagarajan, Bogdan Kustowski, Weng-Keen Wong, Rushil Anirudh

Recent advances in machine learning, specifically transformer architecture, have led to significant advancements in commercial domains.

Suppressing simulation bias using multi-modal data

no code implementations • 19 Apr 2021 • Bogdan Kustowski, Jim A. Gaffney, Brian K. Spears, Gemma J. Anderson, Rushil Anirudh, Peer-Timo Bremer, Jayaraman J. Thiagarajan, Michael K. G. Kruse, Ryan C. Nora

The method described in this paper can be applied to a wide range of problems that require transferring knowledge from simulations to the domain of experiments.

Transfer Learning

Enabling Machine Learning-Ready HPC Ensembles with Merlin

no code implementations • 5 Dec 2019 • J. Luc Peterson, Ben Bay, Joe Koning, Peter Robinson, Jessica Semler, Jeremy White, Rushil Anirudh, Kevin Athey, Peer-Timo Bremer, Francesco Di Natale, David Fox, Jim A. Gaffney, Sam A. Jacobs, Bhavya Kailkhura, Bogdan Kustowski, Steven Langer, Brian Spears, Jayaraman Thiagarajan, Brian Van Essen, Jae-Seung Yeom

With the growing complexity of computational and experimental facilities, many scientific researchers are turning to machine learning (ML) techniques to analyze large scale ensemble data.

BIG-bench Machine Learning Scheduling