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Brian K. Spears

Lawrence Livermore National Laboratory, Livermore, CA

Geometric Priors for Scientific Generative Models in Inertial Confinement Fusion


Nov 24, 2021
Ankita Shukla, Rushil Anirudh, Eugene Kur, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Brian K. Spears, Tammy Ma, Pavan Turaga

* 5 pages, 4 figures, Fourth Workshop on Machine Learning and the Physical Sciences, NeurIPS 2021 

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Transfer learning suppresses simulation bias in predictive models built from sparse, multi-modal data


Apr 19, 2021
Bogdan Kustowski, Jim A. Gaffney, Brian K. Spears, Gemma J. Anderson, Rushil Anirudh, Peer-Timo Bremer, Jayaraman J. Thiagarajan

* 13 pages, 12 figures 

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Meaningful uncertainties from deep neural network surrogates of large-scale numerical simulations


Oct 26, 2020
Gemma J. Anderson, Jim A. Gaffney, Brian K. Spears, Peer-Timo Bremer, Rushil Anirudh, Jayaraman J. Thiagarajan


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Improved Surrogates in Inertial Confinement Fusion with Manifold and Cycle Consistencies


Dec 17, 2019
Rushil Anirudh, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Brian K. Spears

* 10 pages, 6 figures 

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Exploring Generative Physics Models with Scientific Priors in Inertial Confinement Fusion


Oct 03, 2019
Rushil Anirudh, Jayaraman J. Thiagarajan, Shusen Liu, Peer-Timo Bremer, Brian K. Spears

* Machine Learning for Physical Sciences Workshop at NeurIPS 2019 

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Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications


Jul 19, 2019
Shusen Liu, Di Wang, Dan Maljovec, Rushil Anirudh, Jayaraman J. Thiagarajan, Sam Ade Jacobs, Brian C. Van Essen, David Hysom, Jae-Seung Yeom, Jim Gaffney, Luc Peterson, Peter B. Robinson, Harsh Bhatia, Valerio Pascucci, Brian K. Spears, Peer-Timo Bremer


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Contemporary machine learning: a guide for practitioners in the physical sciences


Dec 20, 2017
Brian K. Spears

* 29 pages, 16 figures 

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