Dr. Udo von Toussaint
Phone:+49 89 3299-1817

Garching Site

Group Members

Dirk Nille
Dr. Roland Preuss
Dr. Udo von Toussaint

Plasma-material modelling and foundations

The PMMF group pursues two main lines of research, the interaction of energetic and/or reactive species with surfaces and modern simulation techniques, data analysis methods and optimal experimental design.

The interaction of energetic particles, e.g. hydrogen or helium, with plasma-facing surfaces like tungsten is of crucial importance for the design and development of a fusion power plant. Phenomena like sputtering and erosion of the surface, but also implantation and layer growth may occur and typically exhibit a pronounced dynamics. In addition most of the relevant processes are far from thermodynamic equilibrium. The studies of PMMF target a quantitative understanding of hydrogen retention as well as erosion processes on an atomistic and mesoscopic scale. The latter processes are important to define the plasma boundary conditions, ie. particle and energy influx. Here a close collaboration with other (experimentalist) groups at IPP exist, ie. PWI, PBP and PCI. For its research the group develops and operates a large set of simulation codes, like molecular dynamics (MD), diffusion-reaction models, kinetic Monte-Carlo (KMC) codes and rate-equation models.

Closely related is the other research focus of the group, the design of optimal analysis and measurement strategies (Bayesian experimental design) for computer- and physics experiments. This encompasses not only modern concepts of uncertainty quantification (UQ) of complex computer codes (e.g. Plasma-wall simulations) but also learning systems, which dynamically decide which action (e.g. measurement of a specific spectral line) might yield the most informative future data based on the results from previous actions. This is adressed with Machine Learning techniques, e.g. Hidden Markov Models (HMM), neutral networks or bayesian acyclic graphs and complemented by numerical methods like Markov Chain Monte Carlo (MCMC), sequential optimization or polynomial chaos expansion.

Details to lectures on the research topics of PMMF are given here .

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