Deep Learning Applied on Functional Materials Characterization

Dear Colleagues,

Over the past few decades, deep learning has evolved into one of the most sought out techniques in many areas of research. It has developed into a technique that unites many different fields of science and engineering. The advances in chip processing abilities (GPU) and machine learning algorithms are responsible for the tremendous development observed in deep learning. These advances enable deep learning algorithms to train models from large amounts of data with high efficiency. The processing of multivariate data, image recognition, and processing are some of the applications of deep learning techniques that have attracted researchers to the field of material characterization. The development of advanced and innovative materials also requires advanced characterization techniques. Deep learning techniques can provide the ample support that is required for material characterization. The aim of this Special Issue is to collect high-quality research work on the application of deep learning techniques in material characterization. The topics of interest include, but are not limited to, the following:

• Damage assessment;
• Classification of damages;
• Identification of the stage of the defects;
• Prediction of the lifetime;
• Identification of the material phases;
• Microstructural analysis;
• Non-destructive evaluation;
• Material characterization;
• Civil structures.

Dr. Claudia Barile
Dr. Giovanni Pappalettera
Dr. Vimalathithan Paramsamy Kannan
Topic Editors