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Title: Learning Without Neurons in Physical Systems
Learning is traditionally studied in biological or computational systems. The power of learning frameworks in solving hard inverse problems provides an appealing case for the development of physical learning in which physical systems adopt desirable properties on their own without computational design. It was recently realized that large classes of physical systems can physically learn through local learning rules, autonomously adapting their parameters in response to observed examples of use. We review recent work in the emerging field of physical learning, describing theoretical and experimental advances in areas ranging from molecular self-assembly to flow networks and mechanical materials. Physical learning machines provide multiple practical advantages over computer designed ones, in particular by not requiring an accurate model of the system, and their ability to autonomously adapt to changing needs over time. As theoretical constructs, physical learning machines afford a novel perspective on how physical constraints modify abstract learning theory.  more » « less
Award ID(s):
2011854 2005749
PAR ID:
10418562
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Annual Review of Condensed Matter Physics
Volume:
14
Issue:
1
ISSN:
1947-5454
Page Range / eLocation ID:
417 to 441
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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