Students enrolled in our program have the opportunity to attend a number of seminars during their internship. They cover a wide variety of subjects and are given by leading experts in the field.
Fibers Fabrication and Fiber Lasers
Optical fibers and fiber lasers have unique properties and capabilities that make them key components that are deployed in telecommunication, manufacturing, and sensing. An introduction to optical fibers and fiber lasers will be presented as well as an overview of optical fiber fabrication at LLNL and some examples of unique LLNL fibers.
Targets are a central component to all NIF experiments. The targets themselves are complex little structures that have extreme requirements on quality and precision of all components and their assembly, and the materials required by the experiment often require the development of new materials and processes.
Ignition and Alpha Heating
Producing a burning plasma in the laboratory has been a long-standing milestone for the plasma physics community. A burning plasma is a state where alpha particle deposition from deuterium–tritium (DT) fusion reactions is the leading source of energy input to the DT plasma.
Material Science on NIF
Giant lasers such as the National Ignition Facility are unique tools to study how materials behave at the extreme pressure and temperature conditions that exist deep inside giant planets. To do this we use laser ablation to generate compression waves exceeding the pressure at the center of Jupiter (80 Mbar).
Ultra Short Pulse Lasers
Ultra-short pulses of intense laser light have been used to discover new science since the invention of the laser in 1960. I plan to talk about what we mean when we use terms like “intense” and “ultra-short” and how intensity and pulse duration affect the science we can study with lasers.
Deep Learning/AI and NIF Science
Machine learning is the science of using computers to find relationships in data without explicitly knowing or programming those relationships in advance. Over the last few years, machine learning has found increasingly broad application in the physical sciences.