Institutskolloquium des IPP 2026

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With the introduction of tungsten plasma-facing components in ITER and in other machines, the accurate reconstruction of the tungsten (W) concentration distribution in the plasma has become a critical issue. Indeed, a very low concentration of W in the plasma core can induce a rapid loss of confinement and lead to a disruption or loss of performances. Among the various diagnostic approaches, the use of soft X-ray measurements appears particularly promising. X-ray spectral data can provide access to key plasma parameters, including the magnetic axis position, the electron temperature, and the impurity concentration along with its spatial distribution, following tomographic inversion. Several techniques for reconstructing and validating the tungsten distribution, including recent IA approaches are presented with some application on the WEST tokamak that will be introduced at that occasion. [mehr]

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Recently Gauss Fusion GmbH completed the first phase of our roadmap to a fusion energy power plant in the early 2040's: The conceptual design review (CDR). This CDR detailed the design decisions, unknowns, risks, project breakdown, and developmental planning for a stellarator based fusion power plant (GIGA). The review was presented over the course of three days to a panel of 14 subject area experts from the fusion and nuclear industry. The committee concluded that the design was thorough, well-grounded, systematically structured, realistic, forward thinking, and achievable in the 2040 timeframe. This talk presents and overview of the conceptual design review with a focus on the design and plasma physics of the stellarator. The coil system development, tritium breeding concept, fuel cycle, and the site-selection process will also be presented. [mehr]

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The term "big data" governs not only social media and online stores but also most modern research fields. It obviously also applies to materials science, revolutionizing many of its aspects. But what does "big" mean in the context of typical materials-science machine-learning problems? This question involves not only data volume, but also data quality and veracity as much as infrastructure issues. We ask, how models generalize to similar datasets or how high-quality datasets can be gathered from heterogeneous sources. Likewise, we explore how the feature set and complexity of a model can affect expressivity. And what requirements does this all impose on data infrastructures for creating and hosting large datasets and training models? Through selected examples, I will demonstrate that big data presents unique challenges in many aspects that may often be overlooked but would deserve more attention. I will also discuss how a scalable data infrastructure can make our research data AI ready, and thus contribute to solving the problem. [mehr]