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