Conference Agenda

Machine learning is a valuable tool in tackling a wide variety of problems, including those in computational biomechanics. This pre-course will provide a comprehensive introduction to the basics of machine learning tools and their application in computational biomechanics. The course will cover three topics: 1) constitutive modeling using Gaussian processes, 2) deep learning for image segmentation, and 3) physics-informed neural networks. For each topic, the underlying theory will be complemented with hands-on exercises in Python. The knowledge and experience will help researchers use advanced machine learning tools to improve and automate their computational biomechanical analysis.

Digital Image Correlation (DIC) is a technique based on the recording of images of the surface of a test object exhibiting a contrasted patern of grey levels. Images are recorded either with one (2D) or two (Stereo) cameras and the algorithm processes the images for deformation. This provides thousands of individual displacement values at the studied surface. This can then be used to identify material behaviour, validate numerical models, explore material heterogeneities. This course aims a providing a primer of DIC to this audience to help potential users identify potential areas of use.