|About this Abstract
||MS&T21: Materials Science & Technology
||Accelerating Materials Science with Big Data and Machine Learning
||Topology Optimization for Two-phase Composites Using Active Learning Based Gaussian Process Regression
||Tanu Pittie, Suresh Bishnoi, N. M. Anoop Krishnan
|On-Site Speaker (Planned)
Designing new composite materials is a challenging problem consisting of composition and property prediction, and structure optimization. Recently, machine learning (ML) has emerged as a promising solution for developing design optimization algorithms. However, extensive input data, which is computationally and experimentally expensive, is required to train a reliable ML model for composites. Herein, we use active learning to develop a robust design process for target-specific two-phase composites using sparse data. Finite element analysis is carried out on a handful of randomly generated microstructures to establish the ‘ground truth’. Active learning is used to train a Gaussian process regression model with selective points from the sample space to predict the effective elastic modulus and fracture energy. Promising results close to the ground truth are obtained using very sparse data. It is also shown that the model can learn underlying physics appropriately and hence, can be used for topology optimization of composites.