The projects we work on and those we have completed are the best references for our research work. Fraunhofer SCAI is involved in numerous projects funded by the German Federal Government and the European Commission. The list below presents the projects chronologically – new projects first. You can sort the list by selecting categories.

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  • The PADME-AM, »Partition der Eins Methoden für Additive Manufacturing« (Partition of One Methods for Additive Manufacturing), project is developing new simulation methods for 3D printing processes in the field of laser-based powder bed fusion (L-PBF). L-PBF processes offer the possibility of printing complex components with high quality and low weight for the medical and aerospace industries.

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  • The aim of this project is to significantly accelerate the computational simulation of aging processes in batteries. This is realized by the application of machine learning methods in the control of the solution methods for linear systems of equations. This new autonomous control allows for the usage of efficient iterative methods without a need to accept risks for the robustness of the overall method. Not only existing simulations are accelerated with the improved efficiency of the linear solver, but also model resolutions are made possible that could not be practically used before. The project is carried out in cooperation with the Fraunhofer IEE ( and is funded by the Fraunhofer Research Center for Machine Learning within the Fraunhofer Cluster of Excellence Cognitive Internet Technologies.

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  • The object of this project is to extend the application areas of the SAMG solver package. SAMG already offers Uzawa variants as smoothers in multigrid cycling for saddle point problems in the Navier-Stokes area. For many important problems in the areas of mixed finite element methods, contact and obstacle problems in mechanics, geomechanics or in the area of continuum scale material design (microstructure optimization), the matrices to be solved have a similar structure, but this Uzawa smoothier cannot be used successfully because the physical background of the constraints or saddle point structure is completely different. Here SAMG has to treat certain equations as real algebraic constraints.

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