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ESE Fall Colloquium – “Using Information Geometry to Find Simple Models of Complex Processes”
November 15, 2022 at 10:30 AM - 11:30 AM
Effective theories play a fundamental role in how we reason about the world. Although real physical processes are very complicated, useful models abstract away the irrelevant degrees of freedom to give parsimonious representations. In contrast, overly complex models can be difficult to evaluate, suffer from numerical instabilities, and may overfit data. They also obscure useful insights into the relationship among different physical systems. I use information geometry to explore the role of simplicity in scientific explanation. I interpret a multi-parameter model as a manifold embedded in the space of all possible data, with a metric induced by statistical distance. These manifolds are often bounded and very thin, so they are well-approximated by a low-dimensional, simple model. For many types of models, there is a hierarchy of natural approximations that reside on the manifold’s boundary. These approximations are not black-boxes. They remain expressed in terms of the relevant combinations of mechanistic parameters and reflect the physical principles on which the complicated model was built. They can also be constructed systematically using computational differential geometry, as I illustrate with examples from physics and systems biology
Professor, Physics and Astronomy at Brigham Young University
Dr. Mark Transtrum is an associate professor of Physics and Astronomy at Brigham Young University, where he has been since 2013. Before joining the BYU faculty, he received a PhD in Physics from Cornell in 2011 and then worked as a post-doc in computational biology at MD Anderson Cancer Center in Houston, Texas. His research interests center on methods in mathematical modeling in a variety of interdisciplinary topics. His works addresses model selection and inference problems in power systems, biology, material science, neuroscience, and machine learning.