Enhancing Multi-Objective Optimization through Machine Learning-Supported Multiphysics Simulation

arXiv:2309.13179v2 Announce Type: replace
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 …

abstract arxiv cs.lg datasets framework machine machine learning math.oc multi-objective optimisation optimization paper simulation simulations small speed systems technical through training type world