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Integrability ex machina. (English) Zbl 1537.37104

Summary: Determining whether a dynamical system is integrable is generally a difficult task which is currently done on a case by case basis requiring large human input. Here we propose and test an automated method to search for the existence of relevant structures, the Lax pair and Lax connection respectively. By formulating this search as an optimization problem, we are able to identify appropriate structures via machine learning techniques. We test our method on standard systems of classical integrability and find that we can single out some integrable deformations of a system. Due to the ambiguity in defining a Lax pair our algorithm identifies novel Lax pairs which can be easily verified analytically.
© 2021 The Authors. Fortschritte der Physik published by Wiley-VCH GmbH

MSC:

37M99 Approximation methods and numerical treatment of dynamical systems
37J35 Completely integrable finite-dimensional Hamiltonian systems, integration methods, integrability tests
37K10 Completely integrable infinite-dimensional Hamiltonian and Lagrangian systems, integration methods, integrability tests, integrable hierarchies (KdV, KP, Toda, etc.)
68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.)
68T05 Learning and adaptive systems in artificial intelligence
68T07 Artificial neural networks and deep learning

References:

[1] S.Krippendorf, M.Syvaeri, Machine Learning: Science and Technology2020, 2, 015010 [2003.13679].
[2] S.Greydanus, M.Dzamba, J.Yosinski, Hamiltonian neural networks, 2019.
[3] M.Cranmer, S.Greydanus, S.Hoyer, P.Battaglia, D.Spergel, S.Ho, Lagrangian neural networks, in ICLR 2020 Workshop on Integration of Deep Neural Models and Differential Equations, 2020, 2003.04630, https://openreview.net/forum?id=iE8tFa4Nq.
[4] M.Cranmer, A.Sanchez‐Gonzalez, P.Battaglia, R.Xu, K.Cranmer, D.Spergel, et al., Discovering symbolic models from deep learning with inductive biases, 2020.
[5] P. D.Lax, Communications on Pure and Applied Mathematics1968, 21, 467 [https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpa.3160210503].
[6] L. B.Anderson, M.Gerdes, J.Gray, S.Krippendorf, N.Raghuram, F.Ruehle, Moduli‐dependent Calabi‐Yau and \(S U(3)\)‐structure metrics from Machine Learning, 2012.04656.
[7] N.Beisert, Integrability in QFT and AdS/CFT, https://edu.itp.phys.ethz.ch/hs13/13HSInt/IntHS13Notes.pdf.
[8] E.Abdalla, M. C. B.Abdalla, K. D.Rothe, Non‐Perturbative Methods in 2 Dimensional Quantum Field Theory, 2nd ed. WORLD SCIENTIFIC, 2001, 10.1142/4678, [https://www.worldscientific.com/doi/pdf/10.1142/4678]. · Zbl 0983.81037
[9] K.Pohlmeyer, Commun. Math. Phys.1976, 46, 207. · Zbl 0996.37504
[10] A.Torrielli, J. Phys. A2016, 49, 323001 [1606.02946].
[11] N.Beisert, M.Staudacher, Nucl. Phys. B2003, 670, 439 [hep‐th/0307042]. · Zbl 1058.81581
[12] N.Beisert, C.Ahn, L. F.Alday, Z.Bajnok, J. M.Drummond, L.Freyhult, N.Gromov, R. A.Janik, V.Kazakov, T.Klose, G. P.Korchemsky, C.Kristjansen, M.Magro, T.McLoughlin, J. A.Minahan, R. I.Nepomechie, A.Rej, R.Roiban, S.Schafer‐Nameki, C.Sieg, M.Staudacher, A.Torrielli, A. A.Tseytlin, P.Vieira, D.Volin, K.Zoubos, Lett. Math. Phys.2012, 99, 3 [1012.3982].
[13] A.Guevara, E.Himwich, M.Pate, A.Strominger, Holographic Symmetry Algebras for Gauge Theory and Gravity, 2103.03961. · Zbl 1521.81144
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