A surrogate modeling framework for aircraft assembly deformation using triplet attention-enhanced conditional autoencoder

Abstract

This paper introduces a framework for surrogating aircraft structural deformation using simulation data. The framework compresses high-dimensional field data into embeddings via Principal Component Analysis (PCA) and advanced deep learning methods. It establishes a mapping from discretized control points to these embeddings, enabling complete surrogation from the parameter space to structural deformation. The approach facilitates simultaneous surrogation of both displacement and stress fields, providing a robust evaluation metric for assessing assembly quality. Furthermore, the performance of the proposed PCA and deep learning-based surrogation methods is evaluated using multiple metrics. Results demonstrate that the proposed Conditional Convolutional Autoencoders, enhanced by Triplet attention (C2AE-Tri), achieve higher accuracy and over 60 % data reduction compared to the PCA baseline. This improvement highlights the framework's scalability and utility, particularly when data acquisition is challenging or costly.