  This paper presents a methodological framework for training, self-optimising,
and self-organising surrogate models to approximate and speed up multiobjective
optimisation of technical systems based on multiphysics simulations. At the
hand of two real-world datasets, we illustrate that surrogate models can be
trained on relatively small amounts of data to approximate the underlying
simulations accurately. Including explainable AI techniques allow for
highlighting feature relevancy or dependencies and supporting the possible
extension of the used datasets. One of the datasets was created for this paper
and is made publicly available for the broader scientific community. Extensive
experiments combine four machine learning and deep learning algorithms with an
evolutionary optimisation algorithm. The performance of the combined training
and optimisation pipeline is evaluated by verifying the generated
Pareto-optimal results using the ground truth simulations. The results from our
pipeline and a comprehensive evaluation strategy show the potential for
efficiently acquiring solution candidates in multiobjective optimisation tasks
by reducing the number of simulations and conserving a higher prediction
accuracy, i.e., with a MAPE score under 5% for one of the presented use cases.
