Data to Differential Equations: Discovering Mathematical Models for Biological Systems

Explore a 31-minute conference talk by Liz Livingston, a PhD candidate in Mechanical Engineering and Scientific Computing at the University of Michigan, on discovering mathematical models for biological systems. Delve into the challenges of modeling complex biological phenomena using partial differential equations (PDEs) and learn about innovative approaches to equation discovery. Discover how data-driven methods, including machine learning and inference techniques, can be used to identify governing equations without oversimplifying the system. Gain insights into applications of these tools in complex biological systems, such as flow through stenosed arteries and soft tissue fractures. Understand the speaker's PhD thesis goal of developing and improving mathematical methods to enhance our understanding of intricate biological systems.