Deep learning (DL) is a subset of machine learning. Independent of hand-crafted features like local patterns, a histogram of gradients, etc., DL performs its own patterns establishing and hierarchical information extraction to learn global features layer-wise from data. Beginning from initial layers to learn low-level features, it then moves up the hierarchy to learn a more abstract representation of the data, step by step. This special issue focuses on exploiting thus the capability of DL to build mathematical models depicting the dynamic response of technique systems more objectively and accurately, as well as on developing applications deriving from these mathematical models. The submission should describe original research works in topics and technical areas of interest including but not limited as follows:

    1)  Data-driven based Intelligent Structures (DIS);

    2) Applications of DIS for surveying/applying Smart Materials, Data Science, identification, prediction, measurement, control of technique systems.