In recent years, the integration of deep learning-based universal approximation and traditional numerical methods has led to the development of a new computational science theory, called Hierarchical Deep-learning Neural Network (HiDeNN). This fusion has led to an AI system that delivers unprecedented speed and accuracy compared to traditional numerical methods, particularly for solving complex problems with limited physics and extensive computational requirements. The HiDeNN-AI system offers multi-resolution analysis with automatic adaptivity refinement and built-in Convolutional interpolants for higher-order accuracy. A new mathematical theory, C-HiDeNN-TD, is meticulously designed with controlling parameters such as s-patch size, a-dilation, p-order of polynomial and g-any other interpretable parameters under the HiDeNN-AI framework. The combination of Tensor Decomposition (TD) with Convolution-HiDeNN allows for faster and more accurate solutions to large-scale problems. We will demonstrate the newly developed capabilities of C-HiDeNN-TD by solving a large-scale topological optimization problem, which involves concurrent design and optimization of N-meso-scale lattice structures and M-microscale materials systems. The concurrent design optimization theory (C-HiDeNN-TD-TO) at multiple scales ensures lightweight construction and desired performance, which can be manufactured through 3D printing. To conclude, we will showcase the practical application of this new computational theory in predicting localized materials' plasticity by calibrating the materials law using high-fidelity Digital Image Correlation (DIC) data obtained from experiments.