Adaptive-Quadratic-Neural-Network-Based Multifidelity Modeling Approach for Buckling of Stiffened Panels

This paper presents a method for predicting the buckling load of stiffened panels using multifidelity modeling based on quadratic neural networks (QNNs) with adaptive activation functions. The effectiveness of the proposed approach is demonstrated through a series of simulations on a range of stiffened panel configurations, and the results are compared to those obtained from traditional multifidelity and high-fidelity models in terms of accuracy and computational efficiency. Numerical experiments demonstrate that the model can accurately and efficiently predict the buckling load of stiffened panels while significantly reducing the computational cost of evaluating the surrogate model. In particular, the proposed adaptive quadratic neural networks (AQNNs) model achieves convergence approximately three times faster and four times less trainable parameters compared to traditional artificial neural networks while maintaining the same validation loss. This approach can significantly improve the design and optimization of aerospace structures by easily and quickly exploring various design configurations and finding stable and efficient configurations. This study highlights the potential of a new multifidelity modeling framework for predicting the buckling load and collapse load of aerospace structures by enhancing convergence speed and prediction accuracy while reducing the computational complexity of neural networks.