Machine Learning Applications in Nonlinear Material Physics

Researchers and developers can accelerate the maturation of new materials, technology, or process by applying machine learning techniques. In the area of materials science, one top goal of using such techniques is to better identify and elucidate significant characteristics at different scale steps in the course from manipulate, organization, personalities and presentation. This article outlines how to use automated learning and statistics for addressing select difficulties in continuous materials mechanics. These applications are classified according to the nature of what they do: descriptive, predictive and prescriptive- resembling a narrative based on historical data; trying to predict outcomes at future points in time from observations; or suggesting how business decisions could lead tangible changes for optimizing important features. The choice of the most appropriate machine learning method is determined by elements like use-case, content type, data form and shape complexity (regular, irregular or nature-based/point cloud architecture), geographical AND temporal scales involved; formats used for input-output and knowledge gain we are after apart from usable computing cost. Examples include the use of many artificially constructed share network structures with each other on a need-basis and alongside more data-driven approaches like elementary component evaluation, decisions shrubs, models, woods (or trees), supported learner matrix etc Gaussian learners.