TY - JOUR
T1 - Robust prediction of complex spatiotemporal states through machine learning with sparse sensing
AU - Barmparis, G. D.
AU - Neofotistos, G.
AU - Mattheakis, M.
AU - Hizanidis, J.
AU - Tsironis, G. P.
AU - Kaxiras, E.
N1 - Funding Information:
G.D.B acknowledges support by the “HELLAS-CH” ( MIS Grant No. 5002735 ) implemented under the “Action for Strengthening Research and Innovation Infrastructures,” funded by the Operational Program “Competitiveness, Entrepreneurship and Innovation” (NSRF 2014-2020) and co-financed by Greece and the European Union ( European Regional Development Fund ). G.N. and G.P.T. acknowledge support by the European Commission under project NHQWAVE ( MSCA-RISE 691209 ). J.H. acknowledges support by the General Secretariat for Research and Technology (GSRT) and the Hellenic Foundation for Research and Innovation (HFRI) (Code: 203 ). This work was also partially supported by the Ministry of Education of the Russian Federation in the framework of the Increased Competitiveness Program of NUST “MISiS” (No. K2-2019-010 ), implemented by a governmental decree dated 16th March 2013, N211. G.D.B. and G.N. gratefully acknowledge the hospitality of the Laboratory for Superconducting Metamaterials, NUST “MISiS” where part of this work was performed.
PY - 2020/5/29
Y1 - 2020/5/29
N2 - Complex spatiotemporal states arise frequently in material as well as biological systems consisting of multiple interacting units. A specific, but rather ubiquitous and interesting example is that of “chimeras”, existing in the edge between order and chaos. We use Machine Learning methods involving “observers” to predict the evolution of a system of coupled lasers, comprising turbulent chimera states and of a less chaotic biological one, of modular neuronal networks containing states that are synchronized across the networks. We demonstrated the necessity of using “observers” to improve the performance of Feed-Forward Networks in such complex systems. The robustness of the forecasting capabilities of the “Observer Feed-Forward Networks” versus the distribution of the observers, including equidistant and random, and the motion of them, including stationary and moving was also investigated. We conclude that the method has broader applicability in dynamical system context when partial dynamical information about the system is available.
AB - Complex spatiotemporal states arise frequently in material as well as biological systems consisting of multiple interacting units. A specific, but rather ubiquitous and interesting example is that of “chimeras”, existing in the edge between order and chaos. We use Machine Learning methods involving “observers” to predict the evolution of a system of coupled lasers, comprising turbulent chimera states and of a less chaotic biological one, of modular neuronal networks containing states that are synchronized across the networks. We demonstrated the necessity of using “observers” to improve the performance of Feed-Forward Networks in such complex systems. The robustness of the forecasting capabilities of the “Observer Feed-Forward Networks” versus the distribution of the observers, including equidistant and random, and the motion of them, including stationary and moving was also investigated. We conclude that the method has broader applicability in dynamical system context when partial dynamical information about the system is available.
KW - Chimera state
KW - Machine learning
KW - Prediction
KW - Sparse sensor placement
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U2 - 10.1016/j.physleta.2020.126300
DO - 10.1016/j.physleta.2020.126300
M3 - Article
AN - SCOPUS:85078857500
VL - 384
JO - Physics Letters, Section A: General, Atomic and Solid State Physics
JF - Physics Letters, Section A: General, Atomic and Solid State Physics
SN - 0375-9601
IS - 15
M1 - 126300
ER -