Key Speakers

Prof. Nikolay Kuznetsov
Prof. Nikolay Kuznetsov

Corresponding Member of RAS
St. Petersburg State University,
St.Petersburg, Russia

Keynote: Hidden attractors and global stability in theoretical problems and engineering applications

The development of the theory of global stability, the theory of bifurcations, the theory of chaos, and new computing tech a fresh look at a number of well-known theoretical and practical problems in the analysis of multidimensional dynamical systems theory of hidden oscillations which represents the genesis of the modern era of Andronov’s theory of oscillations. The theory of hid classification of attractors as self-excited or hidden. While trivial attractors (equilibrium points) can be easily found analytically or num chaotic attractors may turn out to be a challenging problem (see, e.g. famous 16th Hilbert problem on the number and disposition of dimensional polynomial systems which is still unsolved). Self-excited attractors can be easily discovered when observing numerical from the vicinity of the equilibria. While hidden attractors have basins of attraction, which are not connected with equilibria, and their of special analytical and numerical methods.

For various applications, the transition of the system’s state to a hidden attractor, caused by external disturbances, may result in the cause of accidents and catastrophes. For various engineering applications the importance of identifying hidden attractors is rel determining the boundaries of global stability in the space of parameters and in the phase space. Outer estimations of the global s parameters and the birth of self-excited oscillations in the phase space can be obtained by the linearization around equilibria and the are related to various well-known conjectures on global stability by the first approximation (see, e.g. Andronov’s proof of the conjec stability by the first approximation, Aizerman and Kalman conjectures). Inner estimations of the global stability boundary can be obtained of global stability. In the gap between outer and inner estimations, there is an exact boundary of global stability which study bifurcations and hidden oscillations.

BIO: Nikolay Vladimirovich Kuznetsov (born 1979, Leningrad, USSR) graduated from Saint Petersburg State University specialist degree in Applied mathematics and informatics (diploma with honors) in 2001. In 2004 he received Candidate of Science degree and in 2016 Doctor of Science degree (Habilitation) from St. Petersburg University. From 2003 till now Nikolay Kuznetsov has been working in St. Petersburg University. Now he is Professor and Head of the Department of Applied Cybernetics. In 2018, Prof. Kuznetsov was elected as the Head of a laboratory in the Institute for Problems in Mechanical Engineering of the Russian Academy of Science. In 2018, the research group chaired by Prof. Kuznetsov was awarded the status of the Leading Scientific School of Russia. Prof. Kuznetsov is a member of the Int. Federation of Automatic Control (IFAC) Policy Committee (2017-2019), a memeber of the Academy of Navigation and Motion Control (since 2012), a member of Editorial Boards of a number of international journals. Prof. Kuznetsov’s research interests are in dynamical systems and applied mathematics. In his works, a combination of rigorous analytical and reliable numerical methods allowed both the advancement in solving previously known fundamental problems as well as the development of modern technologies. Sphere of scientific interests: theory of hidden oscillations, nonlinear control systems, phase synchronization, regular and chaos dynamics. Recent awards: Grant of the President of Russian Federation for the Leading Scientific Schools of Russia (2018-2019), The University of Jyväskylä IT Faculty Medal (2017), Russian Highly Cited Researchers Award (2016, 2017, Web of Science).

Prof. Sergey Bezrodnykh
Prof. Sergey Bezrodnykh

Corresponding Member of RAS
Sternberg Astronomical Institute, MSU,
Federal Research Center
“Computer Science and Control”
of Russian Academy of Sciences,

Moscow, Russia

Keynote: to be announced soon
Prof. Askhat Diveev
Prof. Askhat Diveev

Federal Research Center
“Computer Science and Control”
of Russian Academy of Sciences,

Moscow, Russia

Keynote: Problem of Search for Mathematical Expressions by Symbolic Regression Methods

Several optimization problems require finding a mathematical expression for a function. The class of these optimization problems and the problems involved in constructing numerical methods to solve them are presented. The keynote considers an approach to numerical solution of a problem using symbolic regression methods. It presents a general scheme for searching a mathematical expression using symbolic regression methods, compares several symbolic regression methods, and formulates the properties that symbolic regression methods must have to successfully solve the problem of finding a mathematical expression. The keynote presents a universal principle for designing effective algorithms to search for a mathematical expression while considering prior information. A general approach to constructing algorithms using symbolic regression methods and the principle of small variations are proposed. This approach can be useful in searching for mathematical expressions. Examples of solving the problem of searching for mathematical expressions of functions using symbolic regression methods are provided.

BIO: Askhat Diveev (born in 1954 in Magadan, USSR), Doctor of Technical Sciences, Professor. He graduated from the Bauman Moscow State Technical University (BMSTU) in 1980, in 1989 defended a Candidate thesis in BMSTU, in 2001 he became a Doctor of Technical Sciences in Dorodnitsyn Computing Center of the Russian Academy of Sciences, in 2009 he became a Professor. Main scientific results are following: a new method of symbolic regression for numerical solution of the control synthesis problem called the network operator method (2006), a method of small variations of the code of the basic solution for the optimal solution search in non-numerical space (2006), a theory of controllable networks for traffic flow control models based on network configuration change (2008), new methods of symbolic regression based on modifications of known methods of symbolic regression: variational genetic programming (2014), variational analytical programming (2015), binary variational genetic programming (2016), complete binary variational genetic programming (2017).