INTEGRATING SIMULATION, MACHINE LEARNING, AND EXPERIMENTAL APPROACHES FOR HIGH-THOUGHPUT SMALL-SCALE FRACTURE INVESTIGATIONS
Xing LiuGrand Ballroom B
From Da Vinci to Galileo to modern experimentalists a variety of characterization methods have been introduced for investigating the fracture of materials. Determining fracture properties of materials at small length scales, with complex shapes, under extreme environmental conditions, is still extremely challenging. We will show how this gap is addressed by introducing two novel methods to investigate fracture. The first one involves light for contactless mechanical testing, while the second method integrates experiments with data-driven approaches to address issues related to complex shapes.