%0 Journal Article %1 botache2024enhancing %A Botache, Diego %A Decke, Jens %A Ripken, Winfried %A Dornipati, Abhinay %A Götz-Hahn, Franz %A Ayeb, Mohamed %A Sick, Bernhard %D 2024 %J arXiv e-prints %K imported itegpub isac-www Electric_Motors Multiobjective_Optimisation Surrogate-Modelling Deep-Learning Explainable_Artificial_Intelligence %P arXiv:2309.13179v2 %T Enhancing Multi-Objective Optimization through Machine Learning-Supported Multiphysics Simulation %U https://arxiv.org/abs/2309.13179 %X 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.

@article{botache2024enhancing, abstract = {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.}, added-at = {2024-04-09T11:57:46.000+0200}, archiveprefix = {arXiv}, author = {Botache, Diego and Decke, Jens and Ripken, Winfried and Dornipati, Abhinay and Götz-Hahn, Franz and Ayeb, Mohamed and Sick, Bernhard}, biburl = {https://www.bibsonomy.org/bibtex/2730ae1996394beab35bc8f865eadaf67/ies}, eid = {arXiv:2309.13179v2}, eprint = {2309.13179v2}, interhash = {a934e8d2ece9201cf5998ffcea84f757}, intrahash = {730ae1996394beab35bc8f865eadaf67}, journal = {arXiv e-prints}, keywords = {imported itegpub isac-www Electric_Motors Multiobjective_Optimisation Surrogate-Modelling Deep-Learning Explainable_Artificial_Intelligence}, pages = {arXiv:2309.13179v2}, primaryclass = {cs.LG}, timestamp = {2024-04-09T11:57:46.000+0200}, title = {Enhancing Multi-Objective Optimization through Machine Learning-Supported Multiphysics Simulation}, url = {https://arxiv.org/abs/2309.13179}, year = 2024 }