The pursuit of fusion energy as a clean and virtually limitless power source has gained renewed momentum, particularly following significant advancements in inertial confinement fusion and
magnetic fusion energy technologies. Recent experiments have demonstrated the potential for producing more energy than consumed through nuclear fusion, prompting substantial investments from the U.S. federal government and the private sector. However, the realization of commercially viable fusion power faces substantial mathematical and computational challenges. Current high-fidelity kinetic models, while effective in balancing physics and predictive capabilities, demand immense computational resources, making them impractical for essential tasks like real-time plasma control and experimental design. Multi-fidelity methods that tightly integrate both high-fidelity and lower-fidelity models to optimize computational efficiency while maintaining physical accuracy, promise to be game-changing. These methods are essential for tasks like uncertainty quantification and design optimization, which are crucial for rapid progress in fusion research.
This IPAM Long Program aims to unite mathematicians, physicists, computer scientists, and engineers to collaboratively tackle the challenges and opportunities presented by multi-fidelity modeling in fusion energy research. By leveraging the common language of mathematics, the workshop seeks to expand the community of mathematicians engaged in fusion energy, bringing together areas such as nonlinear dynamics, numerical analysis, machine learning, and uncertainty quantification. The program will provide a platform for sharing knowledge and innovations, ultimately striving to build a multidisciplinary community capable of advancing fusion energy towards practical implementation.
Emily Belli
(General Atomics)
Jonathan Citrin
(DeepMind Technologies)
Ionut-Gabriel Farcas
(University of Texas at Austin)
Lise-Marie Imbert-Gerard
(University of Arizona)
Frank Jenko
(Max Planck Institute for Plasma Physics and UCLA)
Elizabeth Paul
(Columbia University)
Benjamin Peherstorfer
(Courant Institute of Mathematical Sciences)
Cristina Rea
(Massachusetts Institute of Technology)
Tim Wildey
(Sandia National Laboratories)
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