ICF/HED/FPS/Beams: Machine Learning

Presentations

• Stable Machine-Learning Models for Fluid Simulations: A Geometry-Agnostic Approach to Nonlinear Kinetic Effects

  ORAL

  Oct. 30, 2023, 9:30 AM – Oct. 30, 2023, 9:42 AM

  Publication: 1. Joglekar, A. S. & Thomas, A. G. R. Machine learning of hidden variables in multiscale fluid simulation. Submitted to IOP Machine Learning: Science and Technology. Preprint at https://doi.org/10.48550/arXiv.2306.10709 (2023).
  2. Joglekar, A. S. & Thomas, A. G. R. ADEPT - Automatic Differentiation Enabled Plasma Transport. Invited Talk - Synergy of Scientific and Machine Learning Modeling Workshop - ICML (2023).

  Presenters

  • Archis S Joglekar

    • Ergodic LLC

  Authors

  • Archis S Joglekar

    • Ergodic LLC

  • Alexander G Thomas

    • University of Michigan

  View abstract →

• Transformation for Physical Interpretability and Data Refinement in a Machine-Learned NLTE Model of ICF Simulation

  ORAL

  Oct. 30, 2023, 9:42 AM – Oct. 30, 2023, 9:54 AM

  Publication: [1] G. Kluth, et. al., Physics of Plasmas 27 052707 (2020).
  [2] Michael D. Vander Wal, et. al., Machine Learning with Applications 8 100308 (2022).

  Presenters

  • Min Sang Cho

    • Lawrence Livermore National Laboratory

  Authors

  • Min Sang Cho

    • Lawrence Livermore National Laboratory

  • Paul E Grabowski

    • Lawrence Livermore National Laboratory

  • Michael J Barrow

    • Lawrence Livermore National Laboratory

  • Jayram S Thathachar

    • Lawrence Livermore National Laboratory

  • Peer-Timo Bremer

    • Lawrence Livermore National Laboratory

  • Kowshik Thopalli

    • Lawrence Livermore National Laboratory

  • Hai P Le

    • Lawrence Livermore Natl Lab

  • Howard A Scott

    • Lawrence Livermore Natl Lab
    • Lawrence Livermore National Laboratory

  • Mark E Foord

    • Lawrence Livermore Natl Lab

  • Jim A Gaffney

    • Lawrence Livermore National Laboratory

  View abstract →

• Prospects for statistical tests of strong-field quantum electrodynamics with high-intensity lasers.

  ORAL

  Oct. 30, 2023, 9:54 AM – Oct. 30, 2023, 10:06 AM

  Publication: The current work is under submission and a preprint can be found on arXiv :
  
  > Olofsson, Christoffer, and Arkady Gonoskov. "Prospects for statistical tests of strong-field quantum electrodynamics with high-intensity lasers." arXiv preprint arXiv:2303.00568 (2023).

  Presenters

  • Christoffer J Olofsson

    • Department of Physics, University of Gothenburg

  Authors

  • Christoffer J Olofsson

    • Department of Physics, University of Gothenburg

  • Arkady Gonoskov

    • Gothenburg University
    • Department of Physics, University of Gothenburg

  View abstract →

• Quantifying and Reducing Uncertainty in Inertial Confinement Fusion Experiments Using Optimal Experimental Design

  ORAL

  Oct. 30, 2023, 10:06 AM – Oct. 30, 2023, 10:18 AM

  Presenters

  • Codie Y Fiedler Kawaguchi

    • Los Alamos National Laboratory, University of Michigan
    • University of Michigan

  Authors

  • Codie Y Fiedler Kawaguchi

    • Los Alamos National Laboratory, University of Michigan
    • University of Michigan

  • Kirk A Flippo

    • Los Alamos Natl Lab

  • Alexander M Rasmus

    • Los Alamos National Laboratory
    • Los Alamos National Lab

  • Elizabeth C Merritt

    • Los Alamos National Laboratory

  • Eric Johnsen

    • University of Michigan

  • Xun Huan

    • University of Michigan

  View abstract →

• Optimizing New Inertial Confinement Fusion Designs Under Uncertainty

  ORAL

  Oct. 30, 2023, 10:18 AM – Oct. 30, 2023, 10:30 AM

  Presenters

  • Bogdan Kustowski

    • Lawrence Livermore National Laboratory

  Authors

  • Bogdan Kustowski

    • Lawrence Livermore National Laboratory

  • Kelli D Humbird

    • Lawrence Livermore Natl Lab
    • Lawrence Livermore National Laboratory

  • Eugene Kur

    • Lawrence Livermore National Laboratory
    • LLNL

  • Jim A Gaffney

    • Lawrence Livermore National Laboratory

  • Michael K Kruse

    • Lawrence Livermore Natl Lab

  • Ryan C Nora

    • Lawrence Livermore National Laboratory

  • Brian K Spears

    • LLNL

  View abstract →

• Laser-Direct-Drive Cryogenic Implosion Performance on OMEGAand Improvements to Illumination Uniformity and Ablation Pressure

  ORAL

  Oct. 30, 2023, 10:30 AM – Oct. 30, 2023, 10:42 AM

  Presenters

  • Cliff A Thomas

    • University of Rochester

  Authors

  • Cliff A Thomas

    • University of Rochester

  • Christian Stoeckl

    • University of Rochester

  • Michael J Rosenberg

    • Laboratory for Laser Energetics, University of Rochester
    • University of Rochester

  • James P Knauer

    • University of Rochester

  • Wolfgang R Theobald

    • Focused Energy

  • Sean P Regan

    • Laboratory for Laser Energetics, University of Rochester

  • TImothy J Collins

    • University of Rochester

  • Valeri N Goncharov

    • University of Rochester, Laboratory for Laser Energetics

  • Riccardo Betti

    • Laboratory for Laser Energy, Rochester, NY, USA.
    • University of Rochester
    • LLE, Univ of Rochester

  • Christopher Deeney

    • Laboratory for Laser Energetics
    • Laboratory for Laser Energetics, University of Rochester
    • University of Rochester

  • Michael Campbell

    • Laboratory for Laser Energetics
    • MCM Consulting
    • University of Rochester

  • Kenneth Anderson

    • Laboratory for Laser Energetics, U. of Rochester
    • Laboratory for Laser Energetics

  • Katelynn Bauer

    • University of Rochester

  • Mark J Bonino

    • Laboratory for Laser Energetics
    • University of Rochester, Laboratory for Laser Energetics
    • University of Rochester

  • Duc M Cao

    • University of Rochester, Laboratory for Laser Energetics
    • U. Rochester/LLE

  • Stephen Craxton

    • Laboratory for Laser Energetics, University of Rochester
    • University of Rochester

  • Dana H Edgell

    • LLE

  • Reuben Epstein

    • University of Rochester

  • Chad J Forrest

    • University of Rochester

  • Vladimir Glebov

    • Lab for Laser Energetics
    • University of Rochester

  • Varchas Gopalaswamy

    • Laboratory for Laser Energetics - Rochester

  • David R Harding

    • Laboratory for Laser Energetics
    • Laboratory for Laser Energetics, University of Rochester
    • University of Rochester, Laboratory for Laser Energetics
    • University of Rochester

  • Igor V Igumenshchev

    • Lab for Laser Energetics

  • Steven T Ivancic

    • Lab for Laser Energetics

  • Douglas W Jacobs-Perkins

    • University of Rochester

  • Roger T Janezic

    • Laboratory for Laser Energetics
    • University of Rochester

  • Joe Kwiatkowski

    • University of Rochester

  • Aarne Lees

    • University of Rochester

  • Frederic J Marshall

    • University of Rochester

  • Michael Michalko

    • Laboratory for Laser Energetics
    • University of Rochester

  • Dhrumir P Patel

    • University of Rochester

  • Jonathan Peebles

    • University of Rochester

  • P. B Radha

    • Laboratory for Laser Energetics - Rochester
    • University of Rochester
    • Laboratory for Laser Energetics

  • Hans G Rinderknecht

    • Laboratory for Laser Energetics - Rochester

  • Siddharth Sampat

    • Laboratory for Laser Energetics
    • University of Rochester

  • Thomas C Sangster

    • University of Rochester

  • Rahul C Shah

    • Laboratory for Laser Energetics - Rochester

  • Conner A Williams

    • University of Rochester

  View abstract →

• Direct-Drive Implosion Performance Optimization Using Gaussian Process Modeling and Reinforcement Learning

  ORAL

  Oct. 30, 2023, 10:42 AM – Oct. 30, 2023, 10:54 AM

  Presenters

  • Rahman Ejaz

    • Laboratory for Laser Energetics, University of Rochester

  Authors

  • Rahman Ejaz

    • Laboratory for Laser Energetics, University of Rochester

  • Varchas Gopalaswamy

    • Laboratory for Laser Energetics - Rochester

  • Aarne Lees

    • University of Rochester

  • Duc M Cao

    • University of Rochester, Laboratory for Laser Energetics
    • U. Rochester/LLE

  • Soumyendu Sarkar

    • HP Labs, Hewlett Packard Enterprise

  • Christopher Kanan

    • Department of Computer Science, University of Rochester

  View abstract →

• Using Deep Learning to Investigate Laboratory Astrophysics Experiments Through Collective Thomson Scattering Analysis

  ORAL

  Oct. 30, 2023, 10:54 AM – Oct. 30, 2023, 11:06 AM

  Presenters

  • Michael Pokornik

    • University of California, San Diego
    • Lawrence Livermore National Laboratory, Livermore, CA

  Authors

  • Michael Pokornik

    • University of California, San Diego
    • Lawrence Livermore National Laboratory, Livermore, CA

  • Mario Manuel

    • General Atomics - San Diego

  • Kasper Moczulski

    • University of Rochester

  • Petros Tzeferacos

    • University of Rochester

  • Frederico Fiuza

    • Instituto Superior Tecnico (Portugal)

  • Farhat Beg

    • University of California, San Diego
    • University of California San Diego
    • Center for Energy Research UC San Diego, San Diego, CA 92093

  • Alexey V Arefiev

    • University of California, San Diego

  • E. R Tubman

    • Imperial College London
    • Imperial College
    • Imperial College London, London, UK

  • David Larson

    • Lawrence Livermore Natl Lab

  • Bradley B Pollock

    • Lawrence Livermore Natl Lab

  • George F Swadling

    • Lawrence Livermore Natl Lab

  • Drew Higginson

    • Lawrence Livermore Natl Lab
    • Lawrence Livermore National Laboratory

  • Hye-Sook Park

    • LLNL

  View abstract →

• Computational acceleration of ICF capsule simulations through GPU-enabled plasma-physics modules

  ORAL

  Oct. 30, 2023, 11:06 AM – Oct. 30, 2023, 11:18 AM

  Presenters

  • Alejandro Campos

    • Lawrence Livermore Natl Lab

  Authors

  • Alejandro Campos

    • Lawrence Livermore Natl Lab

  • Thomas M Stitt

    • Lawrence Livermore National Laboratory

  • Philip Mocz

    • Lawrence Livermore National Laboratory

  • Aaron A Skinner

    • Lawrence Livermore National Laboratory

  • Arturo Vargas

    • Lawrence Livermore National Laboratory

  • Kristi Belcher

    • Lawrence Livermore National Laboratory

  • David E Stevens

    • Lawrence Livermore National Laboratory

  • John P Grondalski

    • Lawrence Livermore Natl Lab

  • Kenneth Weiss

    • Lawrence Livermore National Laboratory

  • Robert N Rieben

    • Lawrence Livermore Natl Lab

  View abstract →

• Machine-learning based X-ray spectrometer for High Repetition Rate Analysis of Betatron Radiation

  ORAL

  Oct. 30, 2023, 11:18 AM – Oct. 30, 2023, 11:30 AM

  Presenters

  • Nicholas F Beier

    • University of Alberta

  Authors

  • Nicholas F Beier

    • University of Alberta

  • Vigneshvar Senthilkumaran

    • University of Alberta

  • Shubho Mohajan

    • Univ of Alberta

  • Ester Kriz

    • McGill University

  • Ghassan Zeraouli

    • Colorado State University
    • Lawrence Livermore National Laboratory, Colorado State University

  • Sylvain Fourmaux

    • INRS-EMT
    • Institut National de la Recherche Scientifique– Énergie Matériaux et Télécommunications (INRS-EMT)
    • INRS - Energie et Materiaux

  • Francois Legare

    • INRS - Energie et Materiaux

  • Tammy Ma

    • Lawrence Livermore Natl Lab

  • Amina E Hussein

    • University of Alberta, Canada
    • Univ of Alberta

  View abstract →

• Optimization of Polar Direct Drive Illumination for Mega-Joule Laser Facilities

  ORAL

  Oct. 30, 2023, 11:30 AM – Oct. 30, 2023, 11:42 AM

  Publication: We are drafting a publication of the same name to be submitted around the time of the 2023 APS DPP meeting.

  Presenters

  • Duncan Barlow

    • Universite de Bordeaux

  Authors

  • Duncan Barlow

    • Universite de Bordeaux

  • Arnaud Colaitis

    • University of Rochester, LLE

  • Diego Viala

    • University of Bordeaux

  • Julien Mathiaud

    • Universite de Bordeaux

  • Jean-Luc Feugeas

    • Universite de Bordeaux

  • Matthew Streeter

    • Queen's University Belfast

  View abstract →

• Machine Learning Design Optimization of Inner Shells on Double Shell Target Capsules

  ORAL

  Oct. 30, 2023, 11:42 AM – Oct. 30, 2023, 11:54 AM

  Presenters

  • Nomita Vazirani

    • Los Alamos National Lab

  Authors

  • Nomita Vazirani

    • Los Alamos National Lab

  • Ryan F Sacks

    • LANL

  • Brian M Haines

    • Los Alamos National Laboratory
    • LANL
    • Los Alamos Natl Lab

  • Mike Grosskopf

    • Los Alamos National Laboratory

  • David Stark

    • Los Alamos National Laboratory

  • Paul A Bradley

    • Los Alamos Natl Lab

  View abstract →

• Predicting N221204 observables from an ensemble of N210808 simulations

  ORAL

  Oct. 30, 2023, 11:54 AM – Oct. 30, 2023, 12:06 PM

  Presenters

  • Michael K Kruse

    • Lawrence Livermore Natl Lab

  Authors

  • Michael K Kruse

    • Lawrence Livermore Natl Lab

  • Eugene Kur

    • Lawrence Livermore National Laboratory
    • LLNL

  • Jim A Gaffney

    • Lawrence Livermore National Laboratory

  • Kelli D Humbird

    • Lawrence Livermore Natl Lab
    • Lawrence Livermore National Laboratory

  • Bogdan Kustowski

    • Lawrence Livermore National Laboratory

  • Ryan C Nora

    • Lawrence Livermore National Laboratory

  • Brian K Spears

    • LLNL

  View abstract →

• Enhancing Neutron Yield in Cylindrical Target Designs Through Multi-Fidelity Bayesian Optimization

  ORAL

  Oct. 30, 2023, 12:06 PM – Oct. 30, 2023, 12:18 PM

  Presenters

  • William Gammel

    • Los Alamos National Laboratory

  Authors

  • William Gammel

    • Los Alamos National Laboratory

  • Joshua P Sauppe

    • LANL
    • Los Alamos National Laboratory
    • Los Alamos Natl Lab

  • Kevin K Lin

    • University of Arizona

  View abstract →