Renormalization group in physics and beyond

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Antal Jakovac

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Antal Jakovac. (2025). Renormalization group in physics and beyond. International Journal of Physics Research and Applications, 259–262. https://doi.org/10.29328/journal.ijpra.1001132
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Copyright (c) 2025 Jakovac A.

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1. https://doi.org/10.1103/PhysicsPhysiqueFizika.2.263

2. Shalizi CR, Moore C. What is a Macrostate? Subjective Observations and Objective Dynamics. arXiv preprint. 2003. Available from: https://doi.org/10.1007/s10701-024-00814-1

3. Kurbucz MT, Pósfay P, Jakovác A. Facilitating time series classification by linear law-based feature space transformation. Sci Rep. 2022;12(1):18026. Available from: https://doi.org/10.1038/s41598-022-22829-2

4. Jakovác A, Telcs A. Representation and abstraction. Mathematics. 2025;13(10):1666. Available from: https://doi.org/10.3390/math13101666

5. Jakovác A, Telcs A. A note on representational understanding. Entropy. 2022;24(9):1313. Available from: https://doi.org/10.3390/e24091313

6. Wilson KG. The renormalization group: Critical phenomena and the Kondo problem. Rev Mod Phys. 1975;47:773–840. Available from: https://doi.org/10.1103/RevModPhys.47.773

7. Wilson KG, Kogut J. The renormalization group and the ε expansion. Phys Rep. 1974;12(2):75–199. Available from: https://doi.org/10.1016/0370-1573(74)90023-4

8. Cardy J. Scaling and Renormalization in Statistical Physics. Cambridge: Cambridge University Press; 1996. Available from: https://doi.org/10.1017/CBO9781316036440

9. Goldenfeld N. Lectures on Phase Transitions and the Renormalization Group. Boca Raton: CRC Press; 1992. Available from: https://doi.org/10.1201/9780429493492

10. Biró TS, Jakovác A. Entropy of Artificial Intelligence. Universe. 2022;8(1):53. Available from: https://doi.org/10.3390/universe8010053

11. Polchinski J. Renormalization and effective lagrangians. Nucl Phys B. 1984;231:269–95. Available from: https://doi.org/10.1016/0550-3213(84)90287-6

12. Rosten OJ. Fundamentals of the exact renormalization group. Phys Rep. 2012;511(4):177–272. Available from: https://doi.org/10.1016/j.physrep.2011.12.003

13. Mehta P, Schwab DJ. An exact mapping between the variational renormalization group and deep learning. arXiv preprint. 2014. Available from: https://doi.org/10.48550/arXiv.1410.3831

14. Bény C. Deep learning and the renormalization group. arXiv preprint. 2013. Available from: https://doi.org/10.48550/arXiv.1301.3124

15. Lin HW, Tegmark M, Rolnick D. Why does deep and cheap learning work so well? J Stat Phys. 2017;168:1223–47. Available from: https://doi.org/10.1007/s10955-017-1836-5

16. Cammarota C. A renormalization group approach to data analysis. Nat Commun. 2020;11:1573. Available from: https://doi.org/10.1038/s41467-020-15353-8

17. Itzykson C, Zuber J-B. Quantum Field Theory. New York: McGraw-Hill; 1980. Available from: https://archive.org/details/quantumfieldtheo0000itzy

18. French RM. Catastrophic forgetting in connectionist networks. Trends Cogn Sci. 1999;3(4):128–35. Available from: https://doi.org/10.1016/s1364-6613(99)01294-2

19. Kirkpatrick J, Pascanu R, Rabinowitz N, Veness J, Desjardins G, Rusu AA, et al. Overcoming catastrophic forgetting in neural networks. Proc Natl Acad Sci U S A. 2017;114(13):3521–6. Available from: https://doi.org/10.1073/pnas.1611835114