Richard G. Palmer, Professor Emeritus
Office Location: 091 Physics Bldg, Durham, NC 27708
Email Address: palmer@phy.duke.edu
Web Page: http://www.phy.duke.edu/~palmer/
Specialties:
Nonlinear dynamics and complex systems
Theoretical condensed matter physics
Education:
Ph.D., University of Cambridge (United Kingdom), 1973
Theoretical Physics, Cambridge Univ, 1973
Research Categories: Theoretical Condensed Matter Physics
Research Description: Professor Richard G. Palmer is currently working on theories of statistical mechanics. He is interested in the application and development of statistical physics methods for many types of complex systems, including glasses and spin glasses, neural networks, genetic algorithms, and economic markets. The long-term goal of his work is to establish firm theoretical foundations for understanding the emergence of structure, complexity, and computational ability in driven systems of interacting adaptive components. He is also author of two books on the theory of neural networks and on the theory of extinction.
Recent Publications (More Publications)
- Palmer, R, Statistical mechanics approaches to complex optimization problems, in The Economy as an Evolving Complex System: The Proceedings of the Evolutionary Paths of the Global Economy Workshop, Held September, 1987 in Santa Fe, New Mexico (January, 2018), pp. 177-194, CRC Press [doi] [abs].
- Hertz, J; Krogh, A; Palmer, RG, Introduction to the theory of neural computation (January, 2018), pp. 1-327, CRC Press [doi] [abs].
- Anderson, PW; Thouless, DJ; Palmer, RG, Solution of ‘solvable model of a spin glass’, in Career In Theoretical Physics, A (2nd Edition) (January, 2005), pp. 522-530 [doi] [abs].
- M.E.J. Newman and R.G. Palmer, Modeling Extinction (Spring, 2003), Oxford University Press .
- Dragoi, V; Staddon, JER; Palmer, RG; Buhusi, CV, Interval timing as an emergent learning property., Psychological review, vol. 110 no. 1 (January, 2003), pp. 126-144 [doi] [abs].
Highlight:
Professor Richard G. Palmer is currently working on theories of statistical mechanics. He is interested in the application and development of statistical physics methods for many types of complex systems, including glasses and spin glasses, neural networks, genetic algorithms, and economic markets. The long-term goal of his work is to establish firm theoretical foundations for understanding the emergence of structure, complexity, and computational ability in driven systems of interacting adaptive components. He is also author of two books on the theory of neural networks and on the theory of extinction.