Fusion energy holds the promise of providing electricity across the globe in a safe and environmentally friendly way for generations to come. This presupposes, however, that a number of very challenging scientific and technical issues are addressed.
On the physics side, one needs to understand the behavior of ionized gases ("plasmas") at more than 100 million degrees in donut-shaped magnetic fields used for confinement. Given the enormous complexity of fusion systems, computing has played a crucial role in this context ever since the 1960s. In the 2020s, exascale supercomputers are bound to transform fusion research in profound ways, allowing for confinement-by-design and materials-by-design, significantly accelerating progress towards the ultimate fusion energy goal.
In this context, applied mathematics acts as a key enabler: a wide range of approaches, from sparse grid techniques and dimensionality reduction to machine learning and uncertainty quantification, are applied to the broader question: "How can we proceed towards a predictive computational science?"
About the speaker
Prof Frank Jenko is an expert in theoretical and computational plasma physics, with an emphasis on high performance computing. He has authored over 200 peer-review publications, and his research spans plasma and fluid turbulence, magnetic confinement fusion, and space and astrophysical plasma research.
Since 2017 he is Scientific Fellow and head of the Tokamak Theory Division at Max Planck Institute for Plasma Physics.