A data-driven modelling and optimization framework for variable-thickness integrally stiffened shells

TL;DR: A novel design method optimizes large-scale stiffened panels via multi-level dimension reduction, enabling complex curved stiffener layouts and improving load-carrying efficiency by over 80% in aerospace applications, enhancing the upper performance design limit of stiffened structures.

TL;DR: This study proposes an active learning-driven control point optimization method for efficient modeling of complex stiffened curved shells, reducing modeling time by 83.87-66.29% and total time by 42.63-59.35% compared to traditional mesh-mapping techniques.

TL;DR: This paper proposes an advanced rigid-flexible hybrid assembly deviation analysis method (RFHA) for aerostructures, combining rigid deviation transmission and flexible deformation, and utilizing discrete cosine transform, surrogate model, and Jacobian-Torsor model to improve accuracy and reduce computational resources.

TL;DR: BinaryConnect as discussed by the authors proposes to train a DNN with binary weights during the forward and backward propagations, while retaining precision of the stored weights in which gradients are accumulated, and obtain near state-of-the-art results on the permutation-invariant MNIST, CIFAR-10 and SVHN.

TL;DR: This paper systematically compare four popular metamodelling techniques – polynomial regression, multivariate adaptive regression splines, radial basis functions, and kriging – based on multiple performance criteria using fourteen test problems representing different classes of problems.

TL;DR: Comparing and contrast five experimental design types and four approximation model types in terms of their capability to generate accurate approximations for two engineering applications with typical engineering behaviors and a wide range of nonlinearity reveals that uniform designs provide good sampling for generating accurate approxIMations using different sample sizes while kriging models provide accurate approxims that are robust for use with a variety of experimental designs and sample sizes.

TL;DR: The proposed XGBoost–DNN model utilizes X GBoost technique for feature selection followed by deep neural network (DNN) for classification of network intrusion and outperformed over the existing shallow methods used for the dataset.