An adaptive multi-scale computational method for modeling nonlinear deformation in nanoscale materials

In this dissertation a coupled multi-scale computational model for simulating nonlinear deformation processes in crystalline metals at finite temperatures is developed. The computational model uses the finite element method to model the coarse scale response of the material. The constitutive response in the finite element will be modeled through interatomic potentials acting on the underlying homogeneous crystal lattice that characterizes its nanostructure. An adaptive remeshing technique is proposed to automatically delineate regions of severe deformation where homogeneity of the microstructure/deformation is violated. In these regions the finite element will be replaced by a set of deformed atoms which interact with each other through the interatomic potential. The resulting coupled multi-scale model will be used to study defect generation and growth, through a computational nanoindentation experiment, in practical 2D and 3D problems

Research on the Construction of Sales Forecasting Model of Fashion Products Based on Feature Representation of Multimodal and Deep Learning

By improving the accuracy of sales forecasting, this paper provides support for fashion product sales enterprises to make better inventory management and operational decisions. The deep neural network is introduced into the construction of multimodal features, and the internal structure of different modes, such as historical sales features, picture features, and basic attribute features of products, are fully considered, and finally the sales forecasting model of fashion products based on multimodal feature fusion is constructed. In addition, combined with the actual data of the enterprise, the proposed model is compared with the exponential regression model and shallow neural network model. The paper finds that multimodal features and deep learning representation method has better performance than traditional methods (exponential regression and shallow neural network) in the task of predicting sales of fashion products. The results help enterprises use the deep learning method and the data of multiple modal to make accurate sales forecast