Modeling X-ray Superfluorescence by Stochastic Differential Equations

Supervisors: Prof. Nina Rohringer (DESY, UHH), Jun.-Prof. Mathias Trabs (UHH)

X-ray Free-Electron Lasers (XFELs) driven by compact electron accelerators and using short period undulators show complex behavior and are extremely challenging to simulate. Some 10 million electrons are involved in a highly nonlinear motion with collective effects driven by electro-magnetic fields ranging from the mm-wavelength range down to the hard-X-ray wavelength range at a fraction of an Angstrom. To understand and optimize X-ray production with these sources asks for an exact simultaneous solution of the emission and motion of electrons and electro-magnetic fields on multiple time and spatial scales. Even though significant progress has been made in the numerical simulation of particle motion under field effects, substantial progress is still required for XFELs. Data mining, in particular machine learning, offers the potential to accelerate computations, such that a real breakthrough becomes feasible, by inferring knowledge from data bases instead of computing all physical quantities of the system on sufficiently fine spatial and temporal scales. By combining the expertise of the involved research groups, the long-term goal is to achieve the simulations only by providing the physics and geometry of the problem, consequently reducing the mesh and solver parameters setup time to almost zero and eliminating the need of expertise.