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.

翻译：本文提出了一种方法论框架，用于训练、自优化和自组织代理模型，以近似并加速基于多物理场仿真的技术系统多目标优化。基于两个真实数据集，我们证明代理模型可以在相对少量的数据上进行训练，从而准确近似底层仿真。可解释人工智能技术的引入有助于突出特征相关性或依赖性，并支持所用数据集的潜在扩展。其中一个数据集是为本文创建的，并向更广泛的科学界公开。大量实验将四种机器学习和深度学习算法与一种进化优化算法相结合。通过使用真实仿真验证生成的帕累托最优结果，评估了组合训练与优化管道的性能。我们的管道结果及综合评估策略表明，通过减少仿真次数并保持较高的预测精度（例如，在其中一个用例中，MAPE评分低于5%），能够高效获取多目标优化任务中的候选解。