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Nuclear & Plasma Sciences Society

AWARDS

Announcing 2022 TPS Best Paper Award

The winner of the 2022 TPS Best Paper Award has been selected (please refer to our TPS home page for details about the award at https://ieee-npss.org/publications/transactions-on-plasma-science/).  This year is the fourth year that the award is being given, and I am pleased to announce that this year’s winner is the paper, Transfer Learning to Model Inertial Confinement Fusion Experiments, published in IEEE Transactions on Plasma Science, 48:1, pp 61-70, January 2020.  The four co-authors of this paper are Kelli Humbird, J. Luc Peterson, and Brian K. Spears all from the Lawrence Livermore National Laboratory,  Livermore, California USA and Ryan G. McClarren from the University of Notre Dame, Notre Dame, Indiana USA.  The abstract of the paper and the photos and biosketches of the co-authors are given below.  The award plaques, certificates and award checks have been sent to the co-authors by IEEE.  This paper has been made freely available to all our readers.  Congratulations to the team of co-authors Kelli Humbird, J. Luc Peterson, Brian K. Spears, and Ryan G. McClarren on this accomplishment.

ABSTRACT: Inertial confinement fusion (ICF) experiments are designed using computer simulations that are approximations of reality and therefore must be calibrated to accurately predict experimental observations. In this article, we propose a novel technique for calibrating from simulations to experiments, or from low fidelity simulations to high fidelity simulations, via “transfer learning” (TL). TL is a commonly used technique in the machine learning community, in which models trained on one task are partially retrained to solve a separate, but related task, for which there is a limited quantity of data. We introduce the idea of hierarchical TL, in which neural networks trained on low fidelity models are calibrated to high fidelity models, then to experimental data. This technique essentially bootstraps the calibration process, enabling the creation of models which predict high fidelity simulations or experiments with minimal computational cost. We apply this technique to a database of ICF simulations and experiments carried out at the Omega laser facility. TL with deep neural networks enables the creation of models that are more predictive of Omega experiments than simulations alone. The calibrated models accurately predict future Omega experiments, and are used to search for new, optimal implosion designs.

Kelli D. Humbird received a Ph.D. in Nuclear Engineering from Texas A&M University, College Station, TX in 2019.  She is currently a physicist with Lawrence Livermore National Laboratory, Livermore CA, where she works in the inertial confinement fusion program. Her scientific contributions include machine learning analysis of high energy density science experiments, design optimization, machine learning acceleration of Multiphysics codes, and uncertainty quantification.

Luc Peterson received the Ph.D. degree in plasma physics from Princeton University, Princeton, NJ, USA, in 2011. He is currently a Physicist and a Group Leader with the Lawrence Livermore National Laboratory, Livermore, CA, USA, where his scientific contributions include theoretical and computational studies of inertial confinement fusion, hydrodynamic instabilities, radiation transport, hohlraum symmetry, uncertainty quantification, data analytics, high-frequency computing, and machine learning. He is also leading the Merlin Project, which aims to augment computational and experimental workflows with machine learning.

Brian Spears is a physicist at Lawrence Livermore National Laboratory (LLNL).  He is a principal architect of cognitive simulation methods – artificial intelligence (AI) methods that combine high-performance simulation and precision experiments with the goal of improving model predictions. He is also the Director of the LLNL AI Innovation Incubator, AI3.  AI3 develops strong public-private and academic partnerships on collaborative research projects that steering the LLNL AI strategy. In his personal research, he applies cognitive simulation techniques to stockpile stewardship missions with emphasis on quantifying uncertainty in inertial confinement fusion (ICF) experiments and advancing certification methods for the US nuclear weapons stockpile. He also uses cognitive simulation research applications to guide development of next-generation supercomputers. His responsibilities include setting vision for AI development and deployment at the Laboratory while driving LLNL leadership in AI for science.

He has designed ICF experiments for 18 years, including the first cryogenic layered experiments at the National Ignition Facility.  He developed new ICF ignition metrics using the first large-scale ensembles of 2D ICF simulations.  He received the LLNL Mid-Career Recognition for career achievements in research and the Hyperion HPC Innovation Award.  Brian completed his PhD at the University of California, Berkeley where he studied topological methods for high-dimensional dynamical systems.  He also holds a BS in mechanical engineering and a BA in liberal arts from the University of Texas at Austin.  When not doing science, he can be found racing his bike or chauffeuring his two daughters to swim practice.

Ryan McClarren received the Ph.D. degree from the University of Michigan at Ann Arbor, Ann Arbor, MI, USA, in 2007. He is currently, Associate Professor of Aerospace and Mechanical Engineering at the University of Notre Dame. His work applies simulation to understand, analyze, and optimize engineering systems. He has authored numerous publications in refereed journals on machine learning, uncertainty quantification, and numerical methods, as well as three scientific texts: Machine Learning for Engineers, Uncertainty Quantification and Predictive Computational Science: A Foundation for Physical Scientists, and Engineers and Computational Nuclear Engineering and Radiological Science Using Python.  He was recently named Editor-in-Chief of the Journal of Computational & Theoretical Transport.  Prior to joining Notre Dame in 2017, he was Assistant Professor of Nuclear Engineering at Texas A&M University, and previously a research scientist at Los Alamos National Laboratory in the Computational Physics and Methods group.

2022 IEEE/NPSS Radiation Effects Award

Michael Xapsos, NASA GSFC, received the 2022 IEEE/NPSS Radiation Effects Award.

Mike Xapsos joined the Radiation Effects and Analysis group at NASA Goddard Space Flight Center in 2001, where he oversaw its space radiation environment work and supported space flight and research programs until retirement from full time work in 2018.  His work has been used for many NASA missions, including the James Webb Space Telescope, Hubble Space Telescope, Solar Dynamics Observatory and Magnetospheric Multiscale Mission.  He was the Project Scientist for the Living With a Star (LWS) Space Environment Testbed (SET), responsible for the mission scientific objective of improving the performance of space hardware.  Prior to that he worked in the Radiation Effects Branch of the Naval Research Laboratory as a research physicist, investigating the space radiation environment and its effects at the device level.  He received the B.Sc. degree in physics and chemistry from Canisius College in 1978 and the Ph.D. degree in physics from the University of Notre Dame in 1985.

Mike led the development of the ESP/PSYCHIC solar particle event models that are widely used for spacecraft design requirements.  He is the recipient of the NASA Exceptional Engineering Achievement Medal.  He presented Short Courses for the Radiation Effects on Components and Systems (RADECS) Conference, Hardened Electronics and Radiation Technology (HEART) Conference, and for the Nuclear and Space Radiation Effects Conference (NSREC) twice.  He was lead author of an NSREC Outstanding Paper Award and a RADECS Outstanding Conference Oral presentation.  He has been an editor of the IEEE Transactions on Nuclear Science NSREC issue and held various positions for the NSREC including Conference Chair and Technical Program Chair.  He has authored or co-authored over 100 technical publications.

Citation: For contributions to the understanding of space radiation environments and their interactions with microelectronics.

Radiation Effects Nominations for 2023 Awards

Nominations are due January 27th, 2023, for awards that will be presented at the IEEE NSREC 2023 Conference, July 24th – 28th, in Kansas City, Missouri.

Radiation Effects Award Nominations

Nominations are currently being accepted for the 2023 IEEE Nuclear and Plasma Sciences Society (NPSS) Radiation Effects Award. The purpose of the award is to recognize individuals who have had a sustained history of outstanding and innovative technical and/or leadership contributions to the radiation effects community. The $3000 cash award and plaque will be presented at NSREC 2023, Kansas City, Missouri.  Forms are available electronically at http://ieee-npss.org/technical-committees/radiation-effects/ and must be submitted by January 27th, 2023.  Additional information can be obtained from Ruben Garcia, Senior Member-at-Large, CERN, for the Radiation Effects Steering Group.  Ruben can be reached at ruben.garcia.alia@cern.ch

Radiation Effects Early Achievement Award Nominations

Nominations are currently being accepted for the 2023 Radiation Effects Early Achievement Award. The purpose of this award is to recognize an individual early in his or her career whose technical contributions and leadership have had a significant impact on the field of radiation effects.  The $1500 cash award and plaque will be presented at NSREC 2023 in Kansas City, Missouri. Forms are available electronically at http://ieee-npss.org/technical-committees/radiation-effects/ and must be submitted by January 27th, 2023. Additional information can be obtained from Ruben Garcia, Senior Member-at-Large, CERN, for the Radiation Effects Steering Group.  Ruben can be reached at ruben.garcia.alia@cern.ch

Paul Phelps Continuing Education Grant Nominations

Nominations are currently being accepted for the 2023 Paul Phelps Continuing Education Grant. The purpose of the grant is to promote continuing education (attendance at the 2023 NSREC Short Course) and encourage membership in NPSS. Outstanding members of NPSS who are either Student Members, Post-Doctoral Fellows or Research Associates, or unemployed members needing assistance in changing career direction can be nominated for the award. The actual amount of the grant will be determined prior to the 2023 NSREC in Kansas City, Missouri. Funds are to be used towards covering travel costs to attend the NSREC Short Course. The grant also provides complimentary short course registration.

Nomination forms are available electronically at http://ieee-npss.org/technical-committees/radiation-effects/ and must be submitted by January 27th, 2023. Additional information can be obtained from Mike Tostanoski, Member-at-Large, Radiation Test Solutions, for the Radiation Effects Steering Group.  Mike can be reached at mtostanoski@radiationtestsolutions.com

NSREC 2023 Short Course, Kansas City, Missouri

The Short Course Chair is Ethan Cannon, The Boeing Company. The theme of the 2023 course is “Radiation Considerations for Board-Level Computing Systems.”

Presentations and speakers for the four sessions are:

For mor information about the Radiation Effects Committee and its conference and awards, contact Teresa Farris, Vice Chair for Publicity, by E-mail at teresa.farris@archon-llc.com.

IEEE NPSS Foundation Fund

Many Small Donations Add Up

This is a suggestion that you consider taking a moment and make a small donation to the Nuclear and Plasma Sciences Society Fund at the IEEE Foundation. This fund, recently established, is to give the Society much greater flexibility and resources for mainly educational outreach. This can be direct support of students, or expanding the range of focused summer schools like the Instrumentation Summer Schools that have been such a great success in Asia and Africa over the last six years, or new educational initiatives not yet defined. Current IEEE rules force us to change the funding support for these schools after three years and that is where the Foundation Fund comes in.

In order to make transfers into the fund from NPSS operational accounts, IEEE rules require that we not only seek but also get donations from individuals. Thus, your donation, however small, will at least be doubled.

So, head on over to ieeefoundation.org/donate and select “Nuclear and Plasma Sciences Fund” from the drop-down designation box. Remember that for US tax-payers this donation is counted as a charitable donation.

For more information contact Roger Fulton at roger.fulton@sydney.edu.au or Peter Clout at p.clout@ieee.org.

Roger Fulton, Foundation Fund Chair

Peter Clout, Communications Chair