correlations. An mGPR model was subsequently set-up, trained on the experimental dataset and used to predict a cloud of ~ 10 6 input-output parameter matrices. This digital twin acts as a powerful tool for predicting the effects of process adaptions at significantly reduced time and cost: subsequent experimental campaigns and physical set-up alterations can be conducted at a much higher level of information and in a much more targeted way. This was demonstrated by way of example for increasing the target phase yield of δ-Al2O3 while avoiding simultaneous overreaction. In this case, the model is not only able to predict processing windows for achieving this goal. It also provides information on product resilience, and is able to suggest physical alterations in the experimental set-up. While the former is important information regarding the required process stability, the latter provides perspectives and input for process engineering in terms of extending the achievable product range.