We intend to use deep learning methods to build accurate and efficient mechanical models. In other words, we want to train machines to learn mechanics. Once trained, it can then provide fast predictions of mechanical responses of certain systems. The current targeted problem is the mechanical property prediction of meta materials. Meta materials are a new class of man-made materials of which the properties depend on the internal structure of their building blocks instead of their chemical constituents. Accurate and fast prediction of their properties is vital in the design of these materials.
Students are expected to work closely with PhD students on machine training (hyper parameter tuning) and generating ground-truth data using FEM commercial software.
Students will learn (1) machine learning methods, (2) how to construct neural networks and (3) how to use FEM commercial software.