Joint ICF & MFE: Machine Learning and Data Science Technologies

Presentations

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

  ORAL

  Oct. 10, 2024, 9:30 AM – Oct. 10, 2024, 9:54 AM

  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

  Oct. 10, 2024, 9:54 AM – Oct. 10, 2024, 10:06 AM

  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

  Oct. 10, 2024, 10:06 AM – Oct. 10, 2024, 10:18 AM

  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

  Oct. 10, 2024, 10:18 AM – Oct. 10, 2024, 10:30 AM

  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 →

• Simulation-Based Inference of High Field Side Scrape-Off Layer Filament Characteristics using Profile Reflectometry

  ORAL

  Oct. 10, 2024, 10:30 AM – Oct. 10, 2024, 10:42 AM

  Presenters

  • Evan Leppink

    • MIT PSFC

  Authors

  • Evan Leppink

    • MIT PSFC

  • Stephen James Wukitch

    • MIT
    • MIT PSFC

  View abstract →

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

  ORAL

  Oct. 10, 2024, 10:42 AM – Oct. 10, 2024, 10:54 AM

  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

  Oct. 10, 2024, 10:54 AM – Oct. 10, 2024, 11:06 AM

  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 →

• Exploring robust, high yield ICF designs using Bayesian optimization

  ORAL

  Oct. 10, 2024, 11:06 AM – Oct. 10, 2024, 11:18 AM

  Presenters

  • Shailaja Humane

    • University of Michigan

  Authors

  • Shailaja Humane

    • University of Michigan

  • Eugene Kur

    • Lawrence Livermore National Laboratory

  • Kelli D Humbird

    • Lawrence Livermore National Laboratory
    • Lawrence Livermore Natl Lab

  • Carolyn C Kuranz

    • University of Michigan

  View abstract →

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

  ORAL

  Oct. 10, 2024, 11:18 AM – Oct. 10, 2024, 11:30 AM

  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

  Oct. 10, 2024, 11:30 AM – Oct. 10, 2024, 11:42 AM

  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 →

• AI-assisted prediction of laser-plasma instabilities for inertial confinement fusion

  ORAL

  Oct. 10, 2024, 11:42 AM – Oct. 10, 2024, 11:54 AM

  Presenters

  • Chuang Ren

    • University of Rochester

  Authors

  • Chuang Ren

    • University of Rochester

  • Tong Geng

    • University of Rochester

  • Michael C Huang

    • University of Rochester

  • Dongfang Liu

    • Rochester Institute of Technology

  View abstract →

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

  ORAL

  Oct. 10, 2024, 11:54 AM – Oct. 10, 2024, 12:06 PM

  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

  Oct. 10, 2024, 12:06 PM – Oct. 10, 2024, 12:18 PM

  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 →