EXPLORATORY DATA ANALYSIS ON MACROSCOPIC MATERIAL BEHAVIOR USING MICROMECHANICAL SIMULATIONS BY APPLYING THE GAUSSIAN PROCESSES WITH VARIOUS KERNELS
R. Venkatesh Babu, G.Ayyappan Dr., Appleton Dr
Abstract
New materials can bring about tremendous progress in technology and applications. However, the commonly used trial-and-error method cannot meet the current need for new materials. Now, a newly proposed idea of using deductive learning to explore new materials is becoming popular. Deductive learning finds the hidden information in a database. This research work emphases on the capturing the macroscopic material behavior and their relations with the micromechanical simulations are trains the Deductive Learning algorithms. The quality of the Deductive Learning algorithms are only as good as that of the micromechanical model and it is need to validate the new model. It is proposing a novel deductive learning approaches to model macroscopic material behavior using micromechanical simulations to capture the mechanical reply of a variety of microstructures under dissimilar loads.