Joint ICF & MFE: Machine Learning and Data Science Technologies

ORAL · TO07 · ID: 2647417

Presentations

  • Artificial Intelligence-assisted control of Alfvén Eigenmodes improves plasma stability in the DIII-D tokamak

    ORAL

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    Presenters

    • Alvin V Garcia

      Princeton University

    Authors

    • Alvin V Garcia

      Princeton University

    • Azarakhsh Jalalvand

      Princeton University

    • Andy Rothstein

      Princeton University

    • Michael A Van Zeeland

      General Atomics, General Atomics - San Diego

    • Xiaodi Du

      General Atomics

    • Deyong Liu

      General Atomics

    • William Walter Heidbrink

      University of California, Irvine

    • Egemen Kolemen

      Princeton University

    View abstract →

  • Combining physics-based simulations and experimental data from multiple machines to predict and control tokamak profile evolution

    ORAL

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    Presenters

    • Joseph A Abbate

      Princeton Plasma Physics Laboratory

    Authors

    • Joseph A Abbate

      Princeton Plasma Physics Laboratory

    • Egemen Kolemen

      Princeton University

    • Emiliano Fable

      Max Planck Institut fur Plasmaphysik

    • Giovanni Tardini

      Max Planck Institut fur Plasmaphysik

    • Hiro Josep Farre Kaga

      Princeton Plasma Physics Lab, Princeton Plasma Physics Laboratory

    View abstract →

  • Machine Learning model for real-time SPARC vertical stability observers

    ORAL

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    Presenters

    • Arunav Kumar

      Massachusetts Institute of Technology, Australian National University

    Authors

    • Arunav Kumar

      Massachusetts Institute of Technology, Australian National University

    • Cesar F Clauser

      Massachusetts Institute of Technology

    • Theodore Golfinopoulos

      Massachusetts Institute of Technology MI

    • Francesco Carpanese

      Neural Concept

    • A. O Nelson

      Columbia University

    • Darren T Garnier

      OpenStar Technologies

    • Josiah T Wai

      Commonwealth Fusion Systems

    • Dan D Boyer

      Commonwealth Fusion Systems

    • Alex R Saperstein

      Massachusetts Institute of Technology

    • Robert S Granetz

      Massachusetts Institute of Technology

    • Devon J Battaglia

      Commonwealth Fusion Systems

    • Cristina Rea

      Massachusetts Institute of Technology

    View abstract →

  • Results and Lessons Learned from the "Accelerating Radio Frequency Modeling Using Machine Learning" Project

    ORAL

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    Publication: A ́. S ́anchez-Villar et al, Nucl. Fusion . under review,"Real-time capable modelling of ICRF heating on NSTX and WEST via machine learning approaches"

    Wallace et al, ""Towards Fast, Accurate Predictions of RF Simulations via Data-driven Modeling: Forward and Lateral Models" AIP Conf. Proc. 2984, 090008 (2023), https://doi.org/10.1063/5.0162422

    G M Wallace et al. "Towards fast and accurate predictions of radio frequency power deposition and current profile via data-driven modelling: applications to lower hybrid current drive". In: Journal of Plasma Physics 88.4 (2022), p.895880401. DOI: 10.1017/S0022377822000708.

    W. Bethel, eScience 2024 under review, "Case Study: Leveraging GenAI to Build AI-based Surrogates and Regressors for Modeling Radio-Frequency Heating in Fusion Energy Science"

    Presenters

    • John Christopher Wright

      MIT Plasma Science and Fusion Center, Massachusetts Institute of Technology

    Authors

    • John Christopher Wright

      MIT Plasma Science and Fusion Center, Massachusetts Institute of Technology

    • Gregory Marriner Wallace

      MIT Plasma Science and Fusion Center, MIT PSFC

    • G. Pyeon

      MIT

    • E. W. Bethel

      San Francisco State University

    • Vianna Cramer

      SFSU

    • Talita Perciano

      Lawrence Berkeley National Laboratory

    • E. Arias

      LBL

    • R. Sadre

      LBNL

    • Syun'ichi Shiraiwa

      Princeton Plasma Physics Laboratory

    • Nicola Bertelli

      Princeton Plasma Physics Laboratory, Princeton University / Princeton Plasma Physics Laboratory

    • Alvaro Sanchez-Villar

      Princeton University / Princeton Plasma Physics Laboratory, Princeton Plasma Physics Laboratory

    • Alexander del Rio

      San Francisco State University

    • Lothar Narins

      San Francisco State University

    • Chris Pestano

      San Francisco State University

    • Satvik Verma

      San Francisco State University

    View abstract →

  • Physics Informed, Automated and Highly Parallel Bayesian Optimization of Direct-Drive Implosions

    ORAL

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    Presenters

    • Varchas Gopalaswamy

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

    Authors

    • Varchas Gopalaswamy

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

    • Riccardo Betti

      Laboratory for Laser Energetics, University of Rochester, Laboratory for Laser Energy, Rochester, NY, USA.

    • Aarne Lees

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

    • Cliff A Thomas

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

    • Timothy J Collins

      Laboratory for Laser Energetics, University of Rochester

    • Kenneth S Anderson

      Laboratory for Laser Energetics, University of Rochester

    View abstract →

  • Optimizing Cylindrical Targets for Neutron Yield Using Multi-Fidelity Modeling Techniques

    ORAL

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    Publication: W. Gammel, J.P. Sauppe, "Improving Neutron Yield Estimates in Cylindrical Targets through Multi-Fidelity Modeling," in preparation for Physics of Plasmas (2024).

    Presenters

    • William Gammel

      Los Alamos National Laboratory

    Authors

    • William Gammel

      Los Alamos National Laboratory

    • Joshua Paul Sauppe

      Los Alamos National Laboratory

    • Kevin K Lin

      The University of Arizona

    View abstract →

  • Optimizing the Performance of Direct-Drive Implosion Experiments Using Meta-Bayesian Optimization

    ORAL

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    Publication: FusionMamba: A Framework Utilizing Online Policy Adaptation Modules and Mamba for Optimization of Inertial Confinement Fusion Experiments (In preperation for TMLR)

    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, University of Rochester, Laboratory for Laser Energetics - Rochester

    • Ricardo Luna

      Hewlett Packard Labs, Hewlett Packard Enterprise, Milpitas, CA USA

    • Vineet Gundecha

      Hewlett Packard Labs, Hewlett Packard Enterprise

    • Aarne Lees

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

    • Riccardo Betti

      Laboratory for Laser Energetics, University of Rochester, Laboratory for Laser Energy, Rochester, NY, USA.

    • Sahand Ghorbanpour

      Hewlett Packard Labs, Hewlett Packard Enterprise

    • Soumyendu Sarkar

      Hewlett Packard Labs, Hewlett Packard Enterprise

    • Christopher Kanan

      Department of Computer Science, University of Rochester

    View abstract →

  • Experimental Demonstration of 3D Hot-spot Shape Symmetry Control in Laser Direct-Drive Inertial Confinement Fusion Implosions

    ORAL

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    Presenters

    • Ka Ming Woo

      Laboratory for Laser Energetics, University of Rochester

    Authors

    • Ka Ming Woo

      Laboratory for Laser Energetics, University of Rochester

    • Kristen Churnetski

      University of Rochester

    • Riccardo Betti

      Laboratory for Laser Energetics, University of Rochester, Laboratory for Laser Energy, Rochester, NY, USA.

    • Christian Stoeckl

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

    • Cliff A Thomas

      Laboratory for Laser Energetics, University of Rochester, Laboratory for Laser Energetics, University of Rochester Laboratory for Laser Energetics (LLE)

    • Timothy J Collins

      Laboratory for Laser Energetics, University of Rochester

    • Luke A Ceurvorst

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

    • Siddharth Sampat

      Laboratory for Laser Energetics

    • Varchas Gopalaswamy

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

    • Aarne Lees

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

    • Steven T Ivancic

      Lab for Laser Energetics

    • Michael Michalko

      Laboratory for Laser Energetics

    • James P Knauer

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

    • Duc M Cao

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

    • Kenneth S Anderson

      Laboratory for Laser Energetics, University of Rochester

    • Alexander Shvydky

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

    • Rahul C Shah

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

    • Peter V Heuer

      Laboratory for Laser Energetics

    • Sean P Regan

      Laboratory for Laser Energetics, University of Rochester

    • Michael J Rosenberg

      University of Rochester Laboratory for Laser Energetics (LLE), Laboratory for Laser Energetics, University of Rochester, University of Rochester

    View abstract →

  • Predictive Machine Learning Model of Stimulated Brillouin Backscatter at the National Ignition Facility

    ORAL

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    Presenters

    • Eugene Kur

      Lawrence Livermore National Laboratory

    Authors

    • Eugene Kur

      Lawrence Livermore National Laboratory

    • Colin Bruulsema

      Lawrence Livermore National Laboratory

    • Tom D Chapman

      Lawrence Livermore Natl Lab, Lawrence Livermore National Laboratory

    • Nuno Lemos

      Lawrence Livermore Natl Lab

    • Pierre A Michel

      Lawrence Livermore National Laboratory

    • David Jerome Strozzi

      Lawrence Livermore Natl Lab

    View abstract →

  • Comparison of Mo versus W for Double Shell Target Capsules using Machine Learning Optimization

    ORAL

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    Presenters

    • Nomita Vazirani

      Los Alamos National Lab

    Authors

    • Nomita Vazirani

      Los Alamos National Lab

    • Ryan F Sacks

      LANL

    • Brian Michael Haines

      Los Alamos National Laboratory

    • Michael J Grosskopf

      Los Alamos National Lab

    • David Stark

      William & Mary

    • Paul A Bradley

      Los Alamos Natl Lab

    • Eric N Loomis

      Los Alamos Natl Lab, Los Alamos National Laboratory

    • Elizabeth Catherine Merritt

      Los Alamos National Laboratory, Los Alamos National Laboratory (LANL)

    • Harry Francis Robey

      Los Alamos National Laboratory

    View abstract →