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We use information geometry, in which the local distance between models measures their distinguishability from data, to quantify the flow of information under the renormalization group.
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Mitchell J Feigenbaum, Leo P Kadanoff, and Scott J Shenker · 1982
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A geometrical interpretation of renormalization group flow
Brian P Dolan · 1994
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G. Ruppeiner · 1995
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Geometrical aspects of statistical mechanics
D. Brody and N. Rivier · 1995
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Hysteresis, avalanches, and disorder-induced critical scaling: A renormalization-group approach
Karin Dahmen and James P Sethna · 1996
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Vijay Balasubramanian · 1997
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Giovanni Jona-Lasinio · 2001
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Parameter space compression underlies emergent theories and predictive models
B. B. Machta et al · 2013
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Information topology identifies emergent model classes
G Hart MK Transtrum and P Qiu · 2014
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Perspective: Sloppiness and emergent theories in physics, biology, and beyond
MK Transtrum et al · 2015
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Information geometry and the renormalization group
S. Mahapatra R. Maity and T. Sarkar · 2015
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Relative entropy and proximity of quantum field theories
V. Balasubramanian, J. J. Heckman, and Alexander Maloney · 2015
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Information-geometric approach to the renormalization group
Cédric Bény and Tobias J Osborne · 2015
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Methods of Information Geometry
S Amari and H Nagaoka · 2007
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Geometrothermodynamics
Hernando Quevedo · 2007
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Gometry of nonlinear least squares with applications to sloppy models and optimization
MK Transtrum, BB Machta, and JP Sethna · 2011
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We are not considering quantum systems where probabilistic interpretations are less clear [ 28 , 29 ]
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Fully developed isotropic turbulence: Nonperturbative renormalization group formalism and fixed-point solution
Léonie Canet, Bertrand Delamotte, and Nicolás Wschebor · 2016
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Katherine N Quinn, Francesco De Bernardis, Michael D Niemack, and James P Sethna · 2017
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Renormalization group and normal form theory
Archishman Raju, Colin B Clement, Lorien X Hayden, Jaron P Kent-Dobias, Danilo B Liarte, D Rocklin, and James P Sethna · 2017
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