Physical networks: living matter to data science

Catalog description

Physics, engineering, and biology are rife with examples of physical, or material, networks, such as mechanical networks, resistor networks, and flow networks. In these structures, the networks are geometrically embedded, and the physical limitation of space, the position of the nodes, is an important consideration. This course provides an introduction to such systems. The course will cover the basic mathematical tools for network theory, graph theory, and the physics of flow and mechanical networks. Specific systems of great relevance to physics, engineering, and biology, such as mechanical (spring) networks, force chains in jammed packings, the cytoskeleton and other intercellular structural networks, (biological) flow networks, resistor networks, and truss systems will be discussed, as well as dynamics and optimization as applied to these structures. Since these networks are typically complex, the second part of the course will cover a broad array of data analytic techniques to characterize and quantify these structures, such as topological data analysis (TDA) and machine learning.