Machine Learning in Nonlinear Material Physics

Abstract

Researchers and developers can accelerate the development of innovative materials, methods, and procedures by using machine learning technologies. In materials science, one key objective of employing such methods is to make it easier to identify and quantify high features throughout the chain of manipulation, organization, possessions, and efficiency. An overview of effective uses of automated learning and statistics is given in this piece, which addresses specific challenges in continuous materials mechanics. The classification of these applications is based on their nature, categorized as descriptive, predictive, or prescriptive, all aiming to identify, anticipate, or optimize crucial attributes. The selection of the most suitable machine learning technique is influenced by factors such as the unique use case, content type, data characteristics, geographical and temporal scales, formats, targeted knowledge gain, and affordable computing expenses. Various examples are explored, including using various artificially generated share network architectures on an as-needed basis in conjunction with additional data-driven approaches such as basic constituent assessment, decisions shrubs, models, woods, trees, supported matrix, and Gaussian learners.