Multi-fidelity robust design optimisation for composite structures based on low-fidelity models using successive high-fidelity corrections

In this paper, a novel multi-fidelity modelling-based optimisation framework is developed for the robust design of composite structures. The proposed provides significant savings on computation time compared to both conventional and high-fidelity modelling methods while maintaining an acceptable level accuracy. Artificial neural networks (ANNs) multi-level approach are incorporated into formulation. utilises varied High-Fidelity Model (HFM) Low-Fidelity (LFM) covering different spaces. This means that HFM has only few variables, whereas LFM explores entire spaces during process. formulation demonstrated by (RDO) mono-stringer stiffened panel considering uncertainty under non-linear post-buckling regime.