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June 5, 2026
Neural Network Maps as a Method for Constructing Mathematical Models
Scientists from HSE University–Nizhny Novgorod and the Institute of Physics Belgrade, Serbia, are jointly exploring the application of machine learning techniques and neural networks to the study of nonlinear dynamics. Natalya Stankevich, Leading Research Fellow at the Laboratory of Topological Methods in Dynamics of the Faculty of Informatics, Mathematics, and Computer Science at HSE University–Nizhny Novgorod, spoke to the HSE News Service about this international project.
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Dissociation of Exciton States in Warm Dense Hydrogen

JETP Letters. 2021. Vol. 113. No. 6. P. 396–401.
Fedorov I., Stegailov V.
Translator: Kugel K.

The transition from the molecular state to the atomic state in warm dense hydrogen fluid has been actively studied in the past few decades. The use of various experimental techniques has not yet led to reliable results consistent with each other. Despite numerous attempts, theoretical methods have not yet explained existing discrepancies in the experimental data and the microscopic mechanism of the transition of hydrogen fluid to a conducting state. In [I.D. Fedorov, N.D. Orekhov, and V.V. Stegailov, Phys. Rev. B 101, 100101 (R) (2020)], the importance of taking into account nonequilibrium nonadiabatic processes in the analysis of the mechanisms of such transition was demonstrated. In this work, the characteristics of exciton states formed as a result of spontaneous vibronic excitations are calculated. It is shown that the dissociation of such excitons at high temperatures can explain the experimentally observed features of the transition under study.

Research target: Physics
Language: English
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Keywords: фазовые переходыwarm dense hydrogen phase transitionexciton dissociationдиссоциация экситоновплотный водород
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