Regressive and generative neural networks for scalar field theory

Zhou K, Endrödi G, Pang L-G, Stöcker H (2019)
Physical Review D 100(1): 011501.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Autor*in
Zhou, Kai; Endrödi, GergelyUniBi; Pang, Long-Gang; Stöcker, Horst
Abstract / Bemerkung
We explore the perspectives of machine learning techniques in the context of quantum field theories. In particular, we discuss two-dimensional complex scalar field theory at nonzero temperature and chemical potential—a theory with a nontrivial phase diagram. A neural network is successfully trained to recognize the different phases of this system and to predict the values of various observables, based on the field configurations. We analyze a broad range of chemical potentials and find that the network is robust and able to recognize patterns far away from the point where it was trained. Aside from the regressive analysis, which belongs to supervised learning, an unsupervised generative network is proposed to produce new quantum field configurations that follow a specific distribution. An implicit local constraint fulfilled by the physical configurations was found to be automatically captured by our generative model. We elaborate on potential uses of such a generative approach for sampling outside the training region.
Erscheinungsjahr
2019
Zeitschriftentitel
Physical Review D
Band
100
Ausgabe
1
Art.-Nr.
011501
ISSN
2470-0010
eISSN
2470-0029
Page URI
https://pub.uni-bielefeld.de/record/2955718

Zitieren

Zhou K, Endrödi G, Pang L-G, Stöcker H. Regressive and generative neural networks for scalar field theory. Physical Review D. 2019;100(1): 011501.
Zhou, K., Endrödi, G., Pang, L. - G., & Stöcker, H. (2019). Regressive and generative neural networks for scalar field theory. Physical Review D, 100(1), 011501. https://doi.org/10.1103/PhysRevD.100.011501
Zhou, K., Endrödi, G., Pang, L. - G., and Stöcker, H. (2019). Regressive and generative neural networks for scalar field theory. Physical Review D 100:011501.
Zhou, K., et al., 2019. Regressive and generative neural networks for scalar field theory. Physical Review D, 100(1): 011501.
K. Zhou, et al., “Regressive and generative neural networks for scalar field theory”, Physical Review D, vol. 100, 2019, : 011501.
Zhou, K., Endrödi, G., Pang, L.-G., Stöcker, H.: Regressive and generative neural networks for scalar field theory. Physical Review D. 100, : 011501 (2019).
Zhou, Kai, Endrödi, Gergely, Pang, Long-Gang, and Stöcker, Horst. “Regressive and generative neural networks for scalar field theory”. Physical Review D 100.1 (2019): 011501.

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