Instructor: Professor Jim Crutchfield
and Complexity Sciences Center)
Assistant: Jacob Hastings (Physics and Complexity Sciences Center)
Winter 2022: Physics 256A Section 1 (CRN 37775)
Spring 2022: Physics 256B Section 1 (CRN TBD)
Online “flipped” course format:
The course explores how nature's structure reflects how nature computes. It introduces intrinsic unpredictability (deterministic chaos) and the emergence of structure (self-organization) in natural complex systems. Using statistical mechanics, information theory, and computation theory, the course develops a systematic framework for analyzing processes in terms of their causal architecture. This is determined by answering three questions: (i) How much historical information does a process store? (ii) How is that information stored? And (iii) how is the stored information used to produce future behavior? The answers to these questions tell one how a system intrinsically computes.
The course introduces tools to describe and quantify randomness and structure. It shows how they are necessarily complementary and how they are intimately related to concepts from the theory of computation. A number of example complex systemstaken from physics, chemistry, and biologyare used to illustrate the phenomena and methods. The course also takes time to reflect on the intellectual history of these topics, which is quite rich and touches on many basic questions in fundamental physics and the sciences and technology generally. New topics this year include complex materials and computation in quantum systems. The course will bring students to the research frontier in nonlinear physics and complex systems.
Flipped format: Watch lectures and work through interactive labs and homeworks online. Scheduled course time is allocated to hands-on problem solving, discussions on lectures, and introduction to online labs.
Physics of Information: Winter PHY 256A (Course Syllabus [PDF] [HTML])
Complex systems analyzed:
Audience: Graduate students in physics, mathematics, computer science, engineering, mathematical biology, and theoretical neuroscience. Others also welcome.
Prerequisites: Advanced undergraduate or introductory graduate differential equations, applied linear algebra, and probability theory. For example, at UC Davis these are covered in Mathematics 119A/B or 207A, 167 or 226A, and 135A/B or 235A, respectively; or in Physics 104A/B/C or 204A/B.