The purpose of this work package is to:

• Develop robust, stable and explainable data-driven models for physical systems
• Constrain models to enforce meaningful predictions
• Transfer data-driven models from simulations to reality
• Characterise and quantify uncertainty of data-driven models

This work package will develop methods to predict and reduce the uncertainty of data-driven models. The models will be constrained by existing knowledge, allowing to interpret the model (explainable AI) and reducing the amount of required training data. Applying this methods on real world applications will allow the industry partners to better predict the behavior of their facilities and improve their simulations, e.g. for condition monitoring, predictive maintenance, optimal utilization.