The SPP 2422 priority program, established in 2022, aims to develop methods that combine data-driven models with physically based methods. The background to this is the discrepancy between numerical predictions and the process robustness actually achieved in forming technology. Classic simulations reach their limits when complex process noise or large heterogeneous data sets have to be taken into account.

Review of the First Funding Period
In the first phase, the foundations were laid, for example through the use of machine learning for quality prediction. However, the so-called simulation-reality gap became apparent: data models are often not sufficiently generalizable or robust against measurement noise and varying process conditions. The second funding period therefore focuses on hybrid modeling approaches that combine data, physical models, and domain knowledge.

Research Objectives of the Second Funding Period
The plan is to extend existing methods to more complex component geometries and to validate and generalize models within the forming domain. In addition, formalized domain knowledge and explainable AI methods are to be integrated in order to develop assistance systems that specifically optimize the effective areas of forming tools.

The funding phase addresses seven research questions, including the combination of experimental and synthetic data, the formalized integration of process knowledge into AI models, and the generalization of data-driven models to new geometries and materials.

Application Requirements and Deadlines
A two-stage forming process sequence equipped with sensors and a suitable data set is required. Projects must address at least two of the three research priorities: digital representation, information technology automation, or interpretable modeling. The submission deadline is 30 January 2026, and registration on the elan portal is required by 12 January 2026. The review colloquium is expected to take place on 15 April 2026, in Stuttgart.

The DFG welcomes applications from researchers of all backgrounds and particularly encourages female scientists to apply. A prerequisite is the implementation of the code of good scientific practice at the respective institution.

Detailed documentation and guidelines are available here and in the elan portal. The FAQs on application preparation may also be helpful.