Name: Mark Paris
Pronouns: he/him/his
Biography:
I am a theoretical physicist who has been at Los Alamos National Laboratory (in a group called “”T-2″”) for a little over 10 years. I work with colleagues across the laboratory to study systems that are driven primarily by fusion and fission reactions generated within nuclear fuels. This includes systems from astrophysics, the early universe, nuclear safety and security. Nearly all of the problems that we address in T-2 require advanced computational methods and approaches to solve due to their many-body nature. Along with my T-2 colleagues, Amy Lovell and Linda Hlophe, we hope to co-mentor a student in the summer of 2024. Among the three of us, we are continually expanding the purview of computational methods, including machine learning techniques, statistical approaches and uncertainty quantification, ab initio many-body methods, quantum Monte Carlo methods, techniques for solving energy and momentum transport, and other areas. We sometimes handle large amounts of data, in order to compare to the physics models we develop, and we continue to improve on our database structures and interfaces. We all look forward to meeting a diverse array of talented researchers through the SRP.
Institution/Lab: Los Alamos National Laboratory
Website: https://public.lanl.gov/mparis/
SRP Collaboration Topic/Title: Computational methods for applied nuclear reaction theory
Field or research area: Theoretical and computational nuclear physics
Please select all the topical areas that apply to your project:
Computational Science Applications (i.e., bioscience, cosmology, chemistry, environmental science, nanotechnology, climate, etc.); Data Science (i.e., data analytics, data management & storage systems, visualization); High-Performance Computing; Machine Learning and AI
Brief Abstract:
The Theoretical Division at Los Alamos National Laboratory brings a diverse array of expertise to understanding nuclear fusion and fission reactions — the fundamental, underlying component of systems driven by nuclear fuels. Exciting, recent developments in confined fusion systems at facilities around the world demonstrate that terrestrial nuclear fusion is attainable, reinforcing the importance of understanding the basic nuclear reactions governing these systems. In addition to their relevance in the production of terrestrial energy reactors, nuclear reactions are foundational elements in understanding energy production and structure of stars, the “Big Bang” of the early universe, and the extreme phenomena of explosive supernovae and their production of neutron stars and black holes. Our mentoring team (Hlophe, Lovell & Paris) leverages the diverse personal backgrounds and expertise of its members to provide effective mentorship for students from a wide variety of socioeconomic environments and levels of academic preparation. Theoretical Division scientists seek to broaden and deepen our research portfolio in theoretical and computational nuclear physics. We employ standard, few- and many-body techniques in combination with optimization methods, to more efficiently investigate reactions of importance for these sorts of applications in fundamental science, nuclear safety and security, and nuclear energy.
Desired relevant skills, background, or interests:
Interest in, excitement about, and experience in solving physics problems with computers are all welcome.
Other comments:
We are planning to have a co-mentorship model with three T-2 staff members (Amy Lovell, Linda Hlophe, and Mark Paris). Amy is an early career scientist, with T-2 since 2018. Linda Hlophe is taking his first visiting assistant professorship (with MSU) in T-2 since August of this year. Mark Paris has been in T-2 since 2012.
Do any special requirements apply? Permanent Resident OK; International OK
Other, specify:
Keywords:
nuclear theory; computational physics; uncertainty quantification; machine learning; nuclear astrophysics
Lightning Talk Title: Computational methods for applied nuclear reaction theory