1st Computational Physics School for Fusion Research (2019 CPS-FR)

White, Anne; Rea, Cristina; Bonoli, Paul

doi:10.2172/1581130

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1st Computational Physics School for Fusion Research (2019 CPS-FR)

White, Anne·Rea, Cristina·Bonoli, Paul

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Abstract

The rising number of applications of machine learning and computational statistics in fusion energy research requires flexibility in adopting a growing variety of tools. The Computational Physics School for Fusion Research (CPS-FR) aims at providing young researchers with critical skill sets to deal with modern fusion energy research challenges. The School aims at covering essentials of: Computational Statistics, Machine Learning, Deep Learning and optimization methods, Parallel Programming and HPC. As the first edition of the CPS-FR just concluded, this report highlights its main results and summarizes its contents.

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70 PLASMA PHYSICS AND FUSION TECHNOLOGY computational statistics deep learning fusion high performance computing HPC machine learning optimization methods parallel programming

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White, Anne, Rea, Cristina, Bonoli, Paul. 2020-12-03. 1st Computational Physics School for Fusion Research (2019 CPS-FR). https://doi.org/10.2172/1581130

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