Education

The course explores the principles, methodologies, and applications of machine learning (ML) in the domain of mechanics and materials science. By bridging the gap between traditional materials design approaches and the cutting-edge field of machine learning, we learn the tools and knowledge necessary for data-driven discovery and development of materials. Representative ML aspects that are first covered through lectures and live coding include: feed-forward neural networks (NNs), convolutional NNs, recurrent NNs, autoencoding NNs, inductive biases, and best practices in ML.

Next, a wide range of materials science and engineering applications of the above ML topics will be explored via hands-on and latest research-oriented group projects. Some representative examples include (may vary during the course):

• Predicting mechanical behavior of composites and polymers

• Identifying molecules with unique properties

• Designing metamaterials with tailored mechanical behavior

• Characterizing imperfections and defects in additive manufacturing through data and physics