Publikationsrepositorium - Helmholtz-Zentrum Dresden-Rossendorf

Plasma acceleration processes have garnered extensive research interest in recent years
due to the versatile applications of high-energy X-rays, including medical diagnostics and
treatment, semiconductor manufacturing, and the hardening of material surfaces. However,
simulating these processes demands a complex workflow and considerable computational
time, rendering in-situ analysis impractical. To mitigate these challenges, simulation-based
inference and data-driven methods for one-step inversion can be utilized to recover the
matching phase-space representation and elucidate the underlying physics.

This work presents the adaptation of a surrogate model specifically for plasma physics
processes, such as beam transport in a Free Electron Laser (FEL). A previously proposed
model, which uses a mixture of normalizing flows to learn multi-modal distributions, is
systematically investigated. Conditionality as a means of surrogate modeling in plasma
physics is investigated and trained on simulation results of plasma physics processes.

Initial experiments involved using various custom test distributions to derive key insights
into the model’s operation. Conditionality was incorporated into the model by utilizing the
single-view reconstruction provision appropriately, allowing it to handle a wider range of
input conditions. This conditioning has been introduced in two forms, scalar conditioning,
and one-hot vector conditioning, and the effectiveness of each method is studied. This
redefined architecture was rigorously tested on various test distributions with differing
characteristics to validate its performance. The reconstruction quality was then compared
to point clouds generated by single normalizing flow-based models with similar network
sizes. Additionally, a significant reduction in trainable parameters was achieved, making the
model more computationally feasible. Custom training protocols were introduced to further
reduce overall training time.

The enhanced architecture was subsequently applied to simulation data from a free electron
laser. A comprehensive analysis of the results demonstrated the surrogate model’s capability
to accurately capture the complex dynamics of plasma acceleration processes. This work
highlights the potential of advanced surrogate models in reducing computational demands
and providing deeper insights into plasma physics, paving the way for more efficient and
practical applications in high-energy X-ray technologies.