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Towards Data-Driven Optimization of Experiments in Photon Science

Kelling, J.; Willmann, A.; Thiessenhusen, E.; Rustamov, J.; Aguilar, R. A.; Hänel, T.; Hoffmann, N.; Debus, A.; Juckeland, G.; Bachmann, M.

Abstract

High-fidelity (computer-) experiments are very expensive to perform, hence
extracting as much information as possible from the collected data is vital.
We present methods to estimate experiment outcomes based on ingested past data,
to steer further sample acquisition both by suggesting paths for maximized
uncertainty reduction and highlighting sensitivities on inputs, with use-cases
from laser-light propagation and laser-plasma interactions to electron-bunch
kinetics.

Keywords: surrogate models; machine learning; digital twins; laser-plasma accelerators; free-electron laser

Beteiligte Forschungsanlagen

  • Draco
  • Open Access Logo Eingeladener Vortrag (Konferenzbeitrag)
    9th Annual MT meeting, 09.-11.10.2023, Karlsruhe, Deutschland

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Permalink: https://www.hzdr.de/publications/Publ-37652

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