Inductive Design Exploration Method with Active Learning for Complex Design Problems

Abstract

The design of multiscale materials and products has necessitated an inductive and robust design approach to ensure satisfying the performance goals for complex engineering problems. Inductive design exploration method is a performance-driven design approach that explores feasible design spaces while considering the effect of uncertainty that leads to performance variability. However, the existing design method suffers from high computational costs for pre-defined sample data, which sacrifices the accuracy of solution spaces. In this study, we present an improved implementation of the inductive design exploration method by applying the active learning algorithm that is mainly used in machine learning techniques. The purpose of this study is to minimize the sampling effort while maintaining reasonable accuracy in the exploration of design spaces, thereby alleviating computational burden. The capabilities of the improved method are highlighted and demonstrated via a design problem of the blast resistant sandwich panel.

Design of the ONERA M6 wing by shape optimization under uncertainty

“…Fakat bu yöntem, karmaşık problemlerde ve tasarım değişkeninin fazla olduğu durumlarda yüksek hesaplama sürelerine sebebiyet vermektedir. Literatürde olan çalışmalar iki ila beş değişken sayısı arasında değişkenlik göstermektedir [19]. Bu yüzden TTAY'ın yüksek değişken sayısına sahip, belirsizliklerin dahil olduğu karmaşık aerodinamik şekil optimizasyonu problemleri için iyileştirilmesi gerekmektedir.…”