Lecture 1: Monday, 21 August, 09:00 - 10:30
Lecture 2: Tuesday, 22 August, 11:00 – 12:30
Lecture 3: Wednesday, 23 August, 11:00 - 12:30
Title: How materials can learn by themselves
Abstract: In order for artificial neural networks to learn a task, one must solve an inverse design problem. What are all the node weights for a network that will give the desired output? I will demonstrate how approaches developed by computer scientists can be harnessed to solve inverse design problems in soft matter. Specifically, we design mechanical and flow networks to perform functions inspired by biology. But artificial neural networks are constrained by their top-down approach to learning, which requires global minimization of a cost function. I will discuss our recent work pioneering a new approach, bottom-up learning, by which physical systems can learn on their own.
About the speaker:
Andrea Liu is a theoretical soft and living matter physicist who received her A. B. and Ph.D. degrees in physics at the University of California, Berkeley, and Cornell University, respectively. She was a faculty member in the Department of Chemistry and Biochemistry at UCLA for ten years before joining the Department of Physics and Astronomy at the University of Pennsylvania in 2004, where she is the Hepburn Professor of Physics. She is a fellow of the APS, AAAS and the American Academy of Arts and Sciences, and a member of the National Academy of Sciences. Liu has served as Speaker of the Council of the American Physical Society (APS) and Chair of the Physics Section of the American Association for the Advancement of Science (AAAS) and is currently a Councilor of the AAAS and the US National Academy of Sciences.
This lecture is part of the program "Soft and Living Matter: from Fundamental Concepts to New Material Design".