Address: Department of Mathematics, University of Leicester, University
Road, Leicester LE1 7RH, United Kingdom
Institute of Computational Modeling,
Full Professor in
Dynamics of systems of physical, chemical and biological kinetics;
Human adaptation to hard living conditions;
Architecture of neurocomputers and training algorithms for neural networks.
Doctor of Physics & Math (Biophysics), (Advanced
doctoral degree, Dr. Sc., analogue of Dr Habilit.), 1990,
PhD in Physics & Math (Differential Equations
& Math.Phys), 1980,
· Diploma, 1973 (Master degree equivalent), Omsk Pedagogical Institute (Physical Department and Mathematics Department). Thesis: Sets of Removable Singularities in Banach Spaces and Continuous Mappings;
Applied Mathematics Chair,
Name and Address of Current Employe
Dept. of Mathematics,
Chair in Applied Mathematics (2004-present)
Director of the Centre for Mathematical Modelling (2006-present)
· Chief Scientist, 2008-present (on leave);
· Deputy Director and Head of the Computer Sciences Department, 1995 – 2005;
· Head of the Nonequilibrium Systems Laboratory, 1989 – 2008;
· Senior researcher, 1983-1989;
· Junior researcher, 1978-1983;
· Engineer, 1977-1978;
Institute of Theoretical & Applied
· Engineer, 1978;
Tomsk Polytechnic Institute, Laboratory of Kinetics,
· Junior researcher, 1977;
· Junior researcher, 1976;
Omsk Railway Engineering Institute, Research Division,
· Engineer, 1973-1976.
· Head of Neurocomputers Chair, 1993-2006; Professor, 1993-2006 (and now on leave);
Swiss Federal Institute of technology (ETH),
· Senior Researcher, 2003-2004;
· Professor, Department of Automatization and Robots, 1993-2003;
· Professor, Psychological Department, 1998-2001;
· Associate professor, Higher Mathematics Chair, 1981-1989;
· Associate professor, Psychological Department, 1989-1991;
Advisor of the
Clay Mathematics Institute (
Northeastern University (
Courant Mathematics Institute (
· Institut des Hautes Etudes Scientiques (IHES, Paris, France), 10.2000-12.2000, 07.2001-08.2001,11.2002-12-2002, 09.2003;
Swiss Federal Institute of technology (ETH,
Vice-Chairman of Scientific Council at
Head of Workgroup on Neurocomputing, Ministry of
Science and Technology
Vice-Chairman of Expert Council
Chairman of the Analytic Games Committee,
· Member of Jury of USSR National competition in mathematics for students of technical universities (1986-1990).
· Full member of Russian Psychological Association (1989);
Active member of
· Member of Advisory Board of the Russian Neural Networks Society (1990-present);
· Associated Member of ASME (American Society of Mechanical Engineers) (1997);
· Member of Association CHAOS (Centre for Hyperincursion and Anticipation in Ordered Systems) (2000);
· Member of Society for Mathematical Biology (2003).
Participant of 61 conferences, including 15 international, positions as a member of organizing committee or a (co-)chairman at 22 conferences, including 7 international.
International Research workshop: “Principal manifolds for data
cartography and dimension reduction” August 24-26, 2006,
International Workshop “Geometry of Genome: Unravelling of Structures Hidden in Genomic
International Workshop “Model
Reduction and Coarse-Graining Approaches for Multiscale Phenomena,”
International Workshop "Invariance and Model
Reduction for Multiscale Phenomena,"
USA-NIS Neurocomputing Opportunities Workshop,
· Russian annual National Conference “Neurionformatics” (1998-present);
Russian annual National Workshops “Neuroinformatics
Russian annual National Workshops “Modeling of
Russian National Conference “Problems of Regional
· Soviet Union National competition in Neuroinformatics and Neurocomputers for students and young scientists, 1991.
Grants and awards:
Most Cited in 2003-2006 Paper Award for the paper: Gorban, A.N.;Karlin, I.V., Method
of invariant manifold for chemical kinetics, Chem.
Modelling of Adaptation and Decision-Making in Neural Systems, The Royal
Abstraction and Robustness of Network Models in Molecular Biology,
EPSRC and LMS grants for the International Workshop “Model Reduction and Coarse-Graining
Approaches for Multiscale Phenomena,”
Prigogine Prize and Medal (2003,
Clay Scholar, (Clay Mathematics Institute,
· Russian Federal Grant of the “Integration” program, 4 times (1998-2003);
· Grant of Russian Federal subprogram “New Information Processing Technology” (1999);
· Soros Professor (grant of International Science Foundation) (1998);
· Russian Federal Fellowship for outstanding scientists, twice (6 years);
· Grant of Russian Foundation of Basic Research (1996-1998);
Grants of Regional Scientific Foundation,
· 1994-1996 American Mathematical Society Fellowship.
Scientific advisor of 28 PhD thesis and 3 Dr. Habilit. (Dr. Sc.), including:
· David Packwood Non-equilibrium dynamics of lattice-Boltzmann systems (Ph. D., Applied Mathematics, University of Leicester, UK, 2012);
· Jian X. Zhang, nonequilibrium entropic filters for lattice Boltzmann methods and shock tube case studies (Ph. D., Applied Mathematics, University of Leicester, UK, 2011);
· Hafiz Abdul Wahab, Quasichemical Models of Multicomponent Nonlinear Diffusion (Ph. D., Applied Mathematics, University of Leicester, UK, 2011);
· E.M. Mirkes, The structure and functioning of ideal neurocomputer (Dr. Habilt., Computer Science, 2002);
· E.V. Smirnova, Measurement and modeling of adaptation (Dr. Habilt., Modeling in Biophysics, 2001);
· D.A. Rossiev, Neural networks based expert systems for medical diagnostics (Dr. Habilt., Biophysics, 1997);
· A.Yu. Zinovyev, Method of Elastic Maps for Data Visualization: Algorithms, Software and Applications in Bioinformatics (PhD, Computer Science, 2001);
· V.G. Tzaregorodtzev, Algorithms, technology and software for knowledge extraction using trainable neural networks (Ph. D., Computer Science, 2000);
· A.A. Pitenko, Neural networks for geoinformatics (Ph. D., Computer Science, 2000);
· A.A. Rossiev, Neural network modeling of data with gaps (Ph. D., Computer Science, 2000);
· M.Yu. Senashova, Accuracy estimation for neural networks (Ph. D., Computer Science, 1999);
· M.A.Dorrer, Psychological intuition of neural networks (Ph. D., Computer Science, 1999);
· I.V. Karlin, Method of invariant manifold in physical kinetics, (PhD, Physics, 1991);
· V.I.Verbitsky, Simultaneously dissipative operators and global stability (PhD, Mathematical Analysis, 1989);
· M.G. Sadovskii, Optimization in space distributions of populations, (PhD, Biophysics, 1989);
· V.A. Okhonin, Kinetic equations for population dynamics (PhD, Biophysics, 1986).
Leader of 18 full-scale Analytic Games, including:
"Project of a Free Economic Zone for the
"Problems of Russian Culture" (Krasnoyarsk, June 1991);
"Critical Situations in a Transfer to Market" (Krasnoyarsk, December 1990).
Co-organizer of 15
Organizer of 2 Tobolsk Summer Schools for Talented Children.
Monographs (in reverse chronological order):
Models of Catalytic Reactions (Comprehensive
Chemical Kinetics, V.32, ed. by R.G. Compton), Elsevier,
Articles (in reverse chronological order):
1. A.N. Gorban, G.S.Yablonsky, Extended detailed balance for systems with irreversible reactions, Chemical Engineering Science 66 (2011) 5388–5399.
2. A.N. Gorban, H.P. Sargsyan and H.A. Wahab, Quasichemical Models of Multicomponent Nonlinear Diffusion, Mathematical Modelling of Natural Phenomena, Volume 6 / Issue 05, (2011), 184−262.
3. A.Gorban and S. Petrovskii, Collective dynamics: when one plus one does not make two, Mathematical Medicine and Biology (2011) 28, 85−88.
4. A.N. Gorban and M. Shahzad, The Michaelis-Menten-Stueckelberg Theorem. Entropy 2011, 13, 966-1019.
6. A.N. Gorban, Self-simplification in Darwin’s Systems, In: Coping with Complexity: Model Reduction and Data Analysis, A.N. Gorban and D. Roose (eds.), Lecture Notes in Computational Science and Engineering, 75, Springer: Heidelberg – Dordrecht - London -New York, 2011, pp. 311-344.
7. D.J. Packwood, J. Levesley, and A.N. Gorban, Time step expansions and the invariant manifold approach to lattice Boltzmann models, In: Coping with Complexity: Model Reduction and Data Analysis, A.N. Gorban and D. Roose (eds.), Lecture Notes in Computational Science and Engineering, 75, Springer: Heidelberg – Dordrecht - London -New York, 2011, pp. 169-206.
8. A.N. Gorban, Kinetic path summation, multi-sheeted extension of master equation, and evaluation of ergodicity coefficient, Physica A 390 (2011) 1009–1025.
9. A.N. Gorban, L.I. Pokidysheva,·E,V. Smirnova, T.A. Tyukina, Law of the Minimum Paradoxes, Bull Math Biol 73(9) (2011), 2013-2044.
10. A.N. Gorban, E.V. Smirnova, T.A. Tyukina, Correlations, risk and crisis: From physiology to finance, Physica A, Vol. 389, Issue 16, 2010, 3193-3217.
11. A.N. Gorban, A. Zinovyev, Principal manifolds and graphs in practice: from molecular biology to dynamical systems, International Journal of Neural Systems, Vol. 20, No. 3 (2010) 219–232.
12. E. Chiavazzo, I.V. Karlin, A.N. Gorban, K. Boulouchos, Coupling of the model reduction technique with the lattice Boltzmann method, Combustion and Flame 157 (2010) 1833–1849
13. Gorban A.N., Gorban P.A., Judge G. Entropy: The Markov Ordering Approach. Entropy. 2010; 12(5):1145-1193.
14. AN. Gorban and V. M. Cheresiz, Slow Relaxations and Bifurcations of the Limit Sets of Dynamical Systems. I. Bifurcations of Limit Sets, Journal of Applied and Industrial Mathematics, 2010, Vol. 4, No. 1, pp. 54–64.
15. A.N. Gorban and V. M. Cheresiz, Slow Relaxations and Bifurcations of the Limit Sets of Dynamical Systems. II. Slow Relaxations of a Family of Semiflows, Journal of Applied and Industrial Mathematics, 2010, Vol. 4, No. 2, pp. 182–190
16. E. Chiavazzo, I.V. Karlin, and A.N. Gorban, The Role of Thermodynamics in Model Reduction when Using Invariant Grids, Commun. Comput. Phys., Vol. 8, No. 4 (2010), pp. 701-734.
17. Andrei Zinovyev, Nadya Morozova, Nora Nonne, Emmanuel Barillot, Annick Harel-Bellan, Alexander N Gorban, Dynamical modeling of microRNA action on the protein translation process, BMC Systems Biology 2010, 4:13 (24 February 2010).
19. A.N. Gorban, E.V. Smirnova, T.A. Tyukina, General Laws of Adaptation to Environmental Factors: from Ecological Stress to Financial Crisis. Math. Model. Nat. Phenom. Vol. 4, No. 6, 2009, pp. 1-53.
20. A.N. Gorban, A. Y. Zinovyev, Principal Graphs and Manifolds, Chapter 2 in: Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, Emilio Soria Olivas et al. (eds), IGI Global, Hershey, PA, USA, 2009, pp. 28-59.
21. E. Chiavazzo, I. V. Karlin, A. N. Gorban and K Boulouchos, Combustion simulation via lattice Boltzmann and reduced chemical kinetics, J. Stat. Mech. (2009) P06013,
23. A.N. Gorban and O. Radulescu, Dynamic and Static Limitation in Multiscale Reaction Networks, Revisited, Advances in Chemical Engineering 34 (2008), 103-173.
24. A.N. Gorban, Selection Theorem for Systems with Inheritance, Math. Model. Nat. Phenom., Vol. 2, No. 4, 2007, pp. 1-45.
25. R. A. Brownlee, A. N. Gorban, and J. Levesley, Nonequilibrium entropy limiters in lattice Boltzmann methods, Physica A: Statistical Mechanics and its Applications Volume 387, Issues 2-3, 15 January 2008, Pages 385-406 .
26. A.N. Gorban and O. Radulescu, Dynamical robustness of biological networks with hierarchical distribution of time scales, IET Syst. Biol., 2007, 1, (4), pp. 238–246.
27. R. A. Brownlee, A. N. Gorban, and J. Levesley, Stability and stabilization of the lattice Boltzmann method, Phys. Rev. E 75, 036711 (2007) (17 pages)
28. A.N. Gorban and A.Y. Zinovyev The Mystery of Two Straight Lines in Bacterial Genome Statistics, Bulletin of Mathematical Biology (2007)
29. E. Chiavazzo, A.N. Gorban, and I.V. Karlin, Comparison of Invariant Manifolds for Model Reduction in Chemical Kinetics, Commun. Comput. Phys. Vol. 2, No. 5 (2007), pp. 964-992
32. A.N. Gorban and O. Radulescu, Dynamical robustness of biological networks with hierarchical distribution of time scales, IET Syst. Biol., 2007, 1, (4), pp. 238–246
33. R.A. Brownlee, A.N. Gorban, and J. Levesley, Stabilization of the lattice Boltzmann method using the Ehrenfests' coarse-graining idea, Phys. Rev. E 74, 037703 (2006)
34. A.Gorban, I. Karlin, A. Zinovyev, Invariant Grids: Method of Complexity Reduction in Reaction Networks, Complexus, V. 2, 110–127.
35. A.N. Gorban, I.V. Karlin, Quasi-Equilibrium Closure Hierarchies for the Boltzmann Equation, Physica A 360 (2006) 325–364
36. A.Gorban, A. Zinovyev, Elastic Principal Graphs and Manifolds and their Practical Applications, Computing 75, 359–379 (2005),
37. A.N. Gorban, I.V. Karlin, Invariance correction to Grad's equations: Where to go beyond approximations? Continuum Mechanics and Thermodynamics, 17(4) (2005), 311–335,
38. A.N. Gorban, T.G.Popova, A.Yu. Zinovyev, Codon usage trajectories and 7-cluster structure of 143 complete bacterial genomic sequences •Physica A: Statistical and Theoretical Physics, 353C (2005), 365-387.
39. A.N. Gorban, T.G.Popova, A.Yu. Zinovyev, Four basic symmetry types in the universal 7-cluster structure of microbial genomic sequences, In Silico Biology, 5 (2005), 0039.
40. A.N. Gorban, P.A.Gorban, and I. V. Karlin, Legendre Integrators, Post-Processing and Quasiequilibrium, J. Non-Newtonian Fluid Mech. 120 (2004) 149-167.
41. A.N. Gorban, I.V. Karlin, A.Yu. Zinovyev, Constructive methods of invariant manifolds for kinetic problems, Physics Reports, V. 396, N 4-6 (2004), p. 197-403.
42. A.N. Gorban, I.V. Karlin, A.Yu. Zinovyev, Invariant grids for reaction kinetics, Physica A, 333 (2004), 106--154.
43. A.N. Gorban, I.V. Karlin, Uniqueness of thermodynamic projector and kinetic basis of molecular individualism, Physica A, 336, 3-4 (2004), 391-432.
44. A.N. Gorban, I.V. Karlin, Methods of nonlinear kinetics, in: Encyclopedia of Life Support Systems, Encyclopedia of Mathematical Sciences, EOLSS Publishers, Oxford, 2004.
45. A.N. Gorban, T. G. Popova, and A. Yu. Zinovyev: Self-organizing approach for automated gene identification. Open Sys. Information Dyn. 10 (2003) 1-13.
46. A.N. Gorban and I. V. Karlin, Family of additive entropy functions out of thermodynamic limit, Phys. Rev. E. 2003, V.67, 016104, E-print: http:, arXiv.org/abs/cond-mat/0205511
47. A.N. Gorban, I. V. Karlin and H. C. Ottinger, The additive generalization of the Boltzmann entropy. Phys. Rev. E. (2003), V. 67. E-print: http:, arXiv.org/abs/cond-mat/0209319.
48. A.N. Gorban, I. V. Karlin, Method of invariant manifold for chemical kinetics. Chem. Eng. Sci. 58 (2003) 4751-4768.
49. I.V. Karlin, L. L. Tatarinova, A. N. Gorban, and H. C. Öttinger, Irreversibility in the short memory approximation, Physica A 327 (2003) 399-424.
50. A.Gorban, A. Zinovyev, T. Popova. Seven clusters in genomic triplet distributions. In Silico Biology. V.3 (2003), 471-482.
51. A.N. Gorban, T.G Popova, M.G Sadovsky, Classification of nucleotide sequences over their frequency dictionaries reveals a relation between the structure of sequences and taxonomy of their bearers, Zh Obshch Biol 64 (1), 65-77. 2003
52. A.Gorban', Braverman M., Silantyev V. Modified Kirchhoff flow with a partially penetrable obstacle and its application to the efficiency of free flow turbines. Math. Comput. Modelling 35 (2002), No. 13, 1371-1375.
53. Gorban', Silantyev V. Riabouchinsky Flow with Partially Penetrable Obstacle. Math. Comput. Modelling 35 (2002), no. 13, 1365-1370.
54. I.V. Karlin, M. Grmela, and A.N. Gorban: Duality in nonextensive statistical mechanics, Phys. Rev. E 65 (2002) 036128.
55. A.N. Gorban and I. V. Karlin, Reconstruction lemma and fluctuation-dissipation theorem, Revista Mexicana de Fisica, 2002. V. 48 Suplemento 1, PP. 238-242.
56. A.N. Gorban and I. V. Karlin, Geometry of irreversibility, in: Recent Developments in Mathematical and Experimental Physics, Volume C: Hydrodynamics and Dynamical Systems, Ed. F. Uribe (Kluwer, Dordrecht, 2002), pp. 19-43.
57. A.N. Gorban and I. V. Karlin, Macroscopic dynamics through coarse-graining: A solvable example, Phys. Rev. E. V 65. 026116(1-5) (2002).
58. I.V. Karlin and A.N. Gorban, Hydrodynamics from Grad's equations: What can we learn from exact solutions? Ann. Phys. (Leipzig) 10-11 (2002), pp. 783-833. E-print: http:, arXiv.org/abs/cond-mat/0209560
59. A.N. Gorban, Zinov'ev A.Y., Pitenko A.A., Data vizualization. The method of elastic maps, Neirocompjutery, 2002, 4, 19-30.
60. A.N. Gorban, A.A Rossiev, Iterative modeling of data with gaps via submanifolds of small dimension, Neirocompjutery, 2002, 4, 40-44.
61. A.Gorban, Rossiev A., Makarenko N., Kuandykov Y., Dergachev V. Recovering data gaps through neural network methods. International Journal of Geomagnetism and Aeronomy, 2002, Vol. 3, No. 2, December 2002.
62. A.N. Gorban, V.T. Manchuk, A.V.Perfil’eva, E.V.Smirnova, E.P. Cheusova, The mechanism of increasing the correlation between physiological parameters for high adaptation tension, Siberian Ecological Journal, 2001, No 5, 651-655.
63. A.N. Gorban, Gorlov A.M., Silantyev V.M. Limits of the turbin efficiency for free fluid flow, ASME Journal of Energy Resourses Technology, Dec. 2001, V. 123, Iss. 4, pp. 311-317.
64. A.N. Gorban, Pitenko A.A., Zinov'ev A.Y., Wunsch D.C. Vizualization of any data uzing elastic map method , Smart Engineering System Design. 2001, V.11, p. 363-368.
65. A.N. Gorban, Popova T.G., Sadovsky M.G., Wunsch D.C. Information content of the frequency dictionaries, reconstruction, transformation and classification of dictionaries and genetic texts. Smart Engineering System Design, 2001, V.11, p. 657-663.
66. A.N.Gorban, I.V.Karlin, P.Ilg and H.C.Ottinger Corrections and enhancements of quasi-equilibrium states, J. Non-Newtonian Fluid Mech., 2001, V.96(1-2), PP. 203-219.
67. A.N. Gorban, Karlin I.V., Ottinger H.C., Tatarinova L.L. Ehrenfest's argument extended to a formalism of nonequilibrium thermodynaics, Phys. Rev. E. 2001, V. 63. 066124.
68. A.N. Gorban, Gorbunova K.O., Wunsch D.C. Liquid Brain: The Proof of Algorithmic Universality of Quasichemical Model of Fine-Grained Parallelism, Neural Network World, 2001, No. 4. P P. 391-412.
69. A.N. Gorban, Zinovyev A. Yu. Method of Elastic Maps and its Applications in Data Visualization and Data Modeling. International Journal of Computing Anticipatory Systems, CHAOS. 2001. V. 12. PP. 353-369.
70. V.A. Dergachev, Gorban A.N., Rossiev A.A., Karimova L.M., Kuandykov E., Makarenko N.G., Steier. The filling of gaps in geophysical time series by artificial neural networks, Radiocarbon, 2001, V. 43, No. 2, PP. 343-348.
71. A.N.Gorban, V.P.Torchilin, M.V.Malyutov, M. Lu Modeling polymer brushes protective action , Simulation in Industry' 2000. Proceedings of 12-th European Simulation Symposium ESS'2000. September 28-30, 2000, Hamburg, Germany. A publication of the Society of Computer Simulation International. Printed in Delft, The Netherlands, 2000. PP. 651-655.
72. A.N.Gorban, Neuroinformatics: What are us, where are we going, how to measure our way? Informacionnye technologii, 2000, 4. - С. 10-14.
73. A.N. Gorban, K. O. Gorbunova, Liquid Brain: Kinetic Model of Structureless Parallelism, Internation Journal of Computing Anticipatory Systems, CHAOS, V. 6, 2000, P.117-126.
74. A.N. Gorban, I.V. Karlin, V.B. Zmievskii and S.V. Dymova, Reduced description in reaction kinetics, Physica A, 2000. V. 275, No. 3-4, PP. 361-379.
75. A.N Gorban, The generalized Stone approximation theorem for arbitrary algebras of continuous functions, Dokl Akad Nauk, 365 (5), 586-588, 1999
76. A.N. Gorban, A.A Rossiev, Neural network iterative method of principal curves for data with gaps, J Comput Sys Sc Int, 38 (5): 825-830, 1999.
77. A.N. Gorban, I.V.Karlin and V.B.Zmievskii, Two-step approximation of space-independent relaxation, Transp.Theory Stat.Phys., 1999. V. 28(3), PP. 271-296.
78. A.N. Gorban, Approximation of Continuous Functions of Several Variables by an Arbitrary Nonlinear Continuous Function of One Variable, Linear Functions, and Their Superpositions. Appl. Math. Lett., 1998. V. 11, No. 3, pp. 45-49.
79. S.E. Gilev, A.N. Gorban, The completeness theorem for semigroups of continuous functions, Dokl Akad Nauk, 362 (6): 733-734, 1998
80. N.N.Bugaenko, A. N. Gorban, M.G.Sadovskii, Maximum entropy method in analysis of genetic text and measurement of its information content , Open systems and information dynamics. #5, 1998. - pp.265-278.
81. A.N. Gorban, Neuroinformatics and applications, Otkrytye sistemy (Open Systems), 1998, No. 4-5. pp. 36-41.
82. A.N. Gorban, I.V. Karlin, Sroedinger operator in a overfull set , Europhys. Lett., 1998, V. 42, No.2, pp. 113-117.
83. I.V. Karlin, A. N. Gorban, S. Succi, V. Boffi, Maximum Entropy Principle for Lattice Kinetic Equation , Physical Review Letters, 1998, V. 81, No. 1, pp. 6-9.
84. A.N. Gorban, Yeugenii M. Mirkes and Donald Wunsch, High Order Orthogonal Tensor Networks: Information Capacity and Reliability, Proc. IEEE/INNS International Conference on Neural Networks, Houston, IEEE, 1997, pp. 1311-1314.
85. A.N. Gorban, Masha Yu. Senashova and Donald Wunsch, Back-Propagation of Accuracy, Proc. IEEE/INNS International Conference on Neural Networks, Houston, IEEE, 1997, pp. 1998-2001.
86. N.N. Bugaenko, A. N. Gorban, M.G.Sadovskii, Information content of nucleotid sequences and their fragments. Biofizika. 1997. V. 42, Iss. 5, pp. 1047-1053.
87. V.I. Bykov, A.N. Gorban, S.V. Dymova, Method of invariant manifolds for the reduction of kinetic description, ACH-Models Chem 134 (1): 83-95 1997
88. A.N. Gorban, I.V.Karlin, Scattering rates versus moments: Alternative Grad equations, Phys. Rev. E, 1996, 54(4), R3109.
89. A.N. Gorban, I.V.Karlin, Short-Wave Limit of Hydrodynamics: A Soluble Example, Phys. Rev. Lett., 1996, V. 77, N. 2, P. 282-285.
90. N.N. Bugaenko, A.N. Gorban, M.G. Sadovskii, Information content in nucleotide sequences, Mol Biol, 30 (3): 313-320, 1996.
91. A.N. Gorban, T.G. Popova, M.G. Sadovskii, Human virus genes are less redundant than human genes, Genetika, 32 (2), 289-294, 1996.
92. A.N. Gorban, I.V.Karlin, V.B.Zmievskii, T.F.Nonnenmacher, Relaxational trajectories: global approximations, Physica A, 1996, V.231, No.4, p.648-672.
93. A.N. Gorban, D.N.Golub, Multi-Particle Networks for Associative Memory, Proc. of the World Congress on Neural Networks, Sept. 15-18, 1996, San Diego, CA, Lawrence Erlbaum Associates, 1996, pp. 772-775.
94. S.E. Gilev, A. N. Gorban, On Completeness of the Class of Functions Computable by Neural Networks, Proc. of the World Congress on Neural Networks, Sept. 15-18, 1996, San Diego, CA, Lawrence Erlbaum Associates, 1996, pp. 984-991.
95. A.N. Gorban, D.A. Rossiyev, E.V. Butakova, S.E. Gilev, S.E. Golovenkin, S.A. Dogadin, D.A. Kochenov, E.V. Maslennikova, G.V. Matyushin, Y.E. Mirkes, B.V. Nazarov, Medical and Physiological Applications of MultiNeuron Neural Simulator. Proceedings of the 1995 World Congress On Neural Networks, A Volume in the INNS Series of Texts, Monographs, and Proceedings, Vol. 1, 1995.
96. M.G. Dorrer, A.N. Gorban, A.G. Kopytov, V.I. Zenkin, Psychological Intuition of Neural Networks. Proceedings of the 1995 World Congress On Neural Networks, A Volume in the INNS Series of Texts, Monographs, and Proceedings, Vol. 1, 1995.
97. A.N. Gorban, C. Waxman, Neural Networks for Political Forecast. Proceedings of the 1995 World Congress On Neural Networks, A Volume in the INNS Series of Texts, Monographs, and Proceedings, Vol. 1, 1995.
98. A.N. Gorban, T.G. Popova, M.G. Sadovskii, Redundancy of genetic texts, Mol Biol, 28 (2), 206-213, 1994.
99. A.N. Gorban, T.G. Popova, M.G. Sadovskii, Correlation approach to comparing nucleotide-sequences, Zh Obshch Biol, 55 (4-5), 420-430, 1994.
100. A.N. Gorban, I.V. Karlin, General approach to constructing models of the Boltzmann equation, Physica A, 206 (1994), 401-420.
101. A.N. Gorban, I.V. Karlin, Method of invariant manifolds and regularization of acoustic spectra, Transport Theory and Stat. Phys., 23, 559-632, 1994.
102. A.N. Gorban, E.M. Mirkes, T.G. Popova, M.G. Sadovskii, A new approach to the investigations of statistical properties of genetic texts, Biofizika 38 (5), 762-767, 1993.
103. A.N. Gorban, E.M. Mirkes, T.G. Popova, M.G. Sadovskii, The comparative redundancy of genes of various organisms and viruses, Genetika 29 (9), 1413-1419, 1993.
104. A.N. Gorban, I.V.Karlin, Structure and Approximations of the Chapman-Enskog Expansion for Linearized Grad Equations, Transport Theory and Stat.Phys, V.21, No 1&2, P.101-117, 1992.
105. V.I. Verbitskii, A.N. Gorban, Jointly dissipative operators and their applications, Siberian Math J, 33 (1), 19-23, 1992.
106. A.N. Gorban, E.M. Mirkes, A.P. Svitin, Method of multiplet covering and its application for the prediction of atom and molecular-properties, Zh Fiz Khim, 66 (6): 1504-1510, 1992.
107. V.I. Bykov, V.I. Verbitskii, A.N. Gorban, Evaluation of cauchy-problem solution with inaccurately given initial data and the right part, Izv Vuz Mat, (12), 5-8, 1991.
108. A.N. Gorban, V.I.Verbitsky, Simultaneously Dissipative Operators and Quasi-Thermodynamicity of the Chemical Reactions Systems, Advances in Modelling and Simulation, 1992, V.26, N1, p.13-21.
109. N.N. Bugaenko, A. N. Gorban, I.V.Karlin Universal Expansion of the Triplet Distribution Function, Teoreticheskaya i Matematicheskaya Fisica, V.88, No.3, P.430-441(1991).
110. A.N. Gorban, I.V.Karlin, Approximations of the Chapman-Enskog Expansion, Zh.Exp.Teor.Fis., V.100, No.4(10), P.1153-1161(1991); Sov. Phys. JETP, V.73(4), P.637-641.(1991).
111. S.Ye. Gilev, A. N. Gorban and E.M. Mirkes, Small Experts and Internal Conflicts in Learnable Neural Networks, Doklady Acad. Nauk SSSR, V.320, No.1, (1991) P.220-223.
112. A.N. Gorban, E.M. Mirkes, A.N. Bocharov, V.I. Bykov, Thermodynamic consistency of kinetic data, Combust Explosion & Shock, 25 (5), 593-600, 1989.
113. V.I. Verbitskii, A.N. Gorban, G.S. Utiubaev, Y.I. Shokin, Moores effect in interval spaces, Dokl Akad Nauk SSSR, 304 (1), 17-21 1989.
114. A.N. Gorban, M.G. Sadovskii, Optimal strategies of spatial-distribution - Olli effect, Zh Obshch Biol 50 (1), 16-21, 1989.
115. A.N. Gorban, K.R.Sedov and E.V.Smirnova, Correlation Adaptometry as a Method for Measuring the Health, Vestnik Acad. Medic. Nauk SSSR, No.5, P.69-75(1989).
116. V.I.Bykov, A. N. Gorban, A Model of Autooscillations in Association Reactions, Chem.Eng.Sci., V.42, No.5, P.1249-1251(1987).
117. A.N. Gorban, M.G.Sadovskii, Evolutionary Mechanisms of Creation of Cellular Clusters in Flowrate Cultivators, Biotechnology and Biotechnics, No.5, P.34-36(1987).
118. V.I.Bykov, A. N. Gorban, G.S.Yablonskii. Thermodynamic Function Analogue for Reactions Proceeding Without Interactions of Various Substances, Chem.Eng.Sci., V.41, No.11, P.2739-2745 (1986).
119. V.I. Bykov, S.E. Gilev, A.N. Gorban, G.S. Yablonskii, Imitation modeling of the diffusion on the surface of a catalyst, Dokl Akad Nauk SSSR, 283 (5): 1217-1220 1985.
120. V.I. Bykov, A.N. Gorban, Simplest model of self-oscillations in association reactions, React Kinet Catal Lett, 27 (1): 153-155 1985
121. V.I. Bykov, A.N. Gorban, T.P. Pushkareva, Autooscillation model in reactions of the association, Zh Fiz Khim, 59 (2): 486-488, 1985.
122. A.N. Gorban, V.I. Bykov, G.S. Yablonskii, Description of non-isothermal reactions using equations of nonideal chemical-kinetics, Kinet Catal, 24 (5), 1055-1063, 1983.
123. V.I. Bykov, A.N. Gorban, L.P. Kamenshchikov, G.S. Yablonskii, Inhomogeneous stationary states in reaction of carbon-monoxide oxidation on platinum, Kinet Catal, 24 (3), 520-524, 1983
124. V.I. Bykov, A.N. Gorban, Quasithermodynamic characteristic of reactions without the reaction of different substances, Zh Fiz Khim, 57 (12), 2942-2948, 1983.
125. V.I. Bykov, A.N. Gorban, G.S. Yablonskii, Description of non-isothermal reactions in terms of Marcelin-De-Donder kinetics and its generalizations, React Kinet Catal Lett, 20 (3-4), 261-265, 1982.
126. S.E. Gilev, A.N. Gorban, V.I. Bykov, G.S. Yablonskii, Simulative modeling of processes on a catalyst surface, Dokl Akad Nauk SSSR, 262 (6), 1413-1416, 1982.
127. V.I. Elokhin, G.S. Yablonskii, A.N. Gorban, V.M. Ceresiz, Dynamics of chemical-reactions and non-physical steady-states, React Kinet Catal Lett, 15 (2), 245-250, 1980.
128. A.N. Gorban, G.S. Yablonskii, On one unused possibility in the kinetic experiment design, Dokl Akad Nauk SSSR, 250 (5): 1171-1174, 1980.
129. A.N. Gorban, V.I. Bykov, G.S. Yablonskii, The Path to Equilibrium, Intern. Chem. Eng. V.22, No.2, P.386-375(1982).
130. A.N. Gorban, V.M.Ceresiz, Slow Relaxations of Dynamical Systems and Bifurcations of Omega-Limit Sets, Soviet Math. Dokl., V.24, P.645-649(1981).
131. A.N. Gorban, V.I. Bykov, G.S. Yablonskii, Macroscopic Clusters Induced by Diffusion in Catalytic Oxidation Reactions, Chem. Eng. Sci., 1980. V. 35, N. 11. P. 2351-2352. .
132. A.N. Gorban, V.I.Bykov, V.I.Dimitrov. Marcelin-De Donder Kinetics Near Equilibrium, React. Kinet. Catal. Lett., V.12, No.1, P.19-23(1979).
133. A.N. Gorban, Priori evaluation of the region of linearity for kinetic-equations, React Kinet Catal Lett, 10 (2), 149-152, 1979
134. A.N. Gorban, Invariant Sets for Kinetic Equations, React. Kinet. Catal. Lett., 1979, V.10, P.187-190.
135. A.N. Gorban, Sets of Removable Singularities and Continuous Mappings, Siberian Math. Journ., V.19, P.1388-1391(1978).
136. A.N. Gorban, V.B. Melamed, Certain properties of Fredholm analytic sets in Banach-spaces, Siberian Math J, 17 (3), 523-526, 1976.
Past Achievements and Future Research
A collection of methods for construction of slow invariant manifolds has been developed, in particular the analogue of Kolmogorov-Arnold-Moser methods for dissipative systems. The nonperturbative deviation of physically consistent hydrodynamics from the Boltzmann equation and from reversible dynamics, for Knudsen numbers near one, was obtained.
The theory of simultaneously dissipative operators and tools for global stability analysis were developed. An explicitly solvable mathematical model for estimating the maximum efficiency of turbines in a free (non-ducted) fluid was obtained. This result can be used for hydropower turbines where construction of dams is impossible or undesirable.
A family of fast training algorithms for neural networks
and generalized technology of extraction of explicit knowledge from data was
developed. These algorithms are now in use in medical expert systems and in
anti-terrorism security systems in
The geometric seven-cluster structure of the genome was discovered.
The Geometry of Irreversibility. A new general geometrical framework of nonequilibrium thermo-dynamics will be developed. Our approach is based on constructive methods of invariant manifolds elaborated during the past two decades. The new methods allow us to solve the problem of macro-kinetics even when there are no autonomous equations of macro-kinetics. These methods will be elaborated together with computational algorithms. Each step of these algorithms should be physically consistent. The notion of the invariant film of non-equilibrium states, and the method of its approximate construction transform the problem of nonequilibrium kinetics into a series of problems of equilibrium statistical physics. The main specific problem for application of developed methods will be the problem of dynamic memory appearance in macromolecular complexes. Such memory effects may be important for chromatin dynamics and its role in functional nuclear organization. Spatio-temporal organization of chromatin will be studied.
Results and Projects (1971-2004)
1. The beginning (1971-1975)
Two scientific contacts determined
my scientific work during 1971-1975: Prof. V.P. Mikheev (technical sciences)
and Prof. V.B. Melamed (functional analysis). With Prof. Mikheev we created
models of contact net and contact devices and developed new stations for
technical diagnosis. Perhaps the main results of our collaboration are:
stations for technical diagnosis that were in use on the
Prof. Melamed was from the
2. Chemical kinetics and topological dynamics (1975-1980)
3. Biological kinetics and functional analysis (1980-1990)
Does the dynamics of
distributed systems which models biological evolution always lead to a discrete
distribution? (In the biological context this question can be reformulated as
follows: is natural selection really effective if the initial diversity is
sufficiently rich?) In order to answer
this question, a theory of special dynamical systems in the space of Radon measures
on compact space was developed. These
are systems with a specific conservation law: the conservation of support of
measures. There are characterization theorems for omega-limit points, and
different theorems about efficiency of natural selection. The qualitative
picture of these results was summarized in the book: Demon of
This abstract theory has
found very practical application. My former PhD student, E. V. Smirnova (now
Professor Smirnova) discovered that the approximate dimension of the cloud of
physiological data of a group precisely characterizes the level of adaptation
of this group to the living conditions: when the group members exhaust their
adaptation resource then the dimension usually decreases. It decreases usually, but not always. Sometimes the dimension goes another way. We
explained the effect, and, on the other hand, predicted the exclusions. The
results were confirmed by thousands of experiments with different populations
and groups: from human to plants and fungi. Now the developed concept of correlation adaptometry is in use for
monitoring needs in
4. Neural networks (1985-now)
In 1985 I stated the problem of effective parallelism as a main problem for our group for the next decade. In 1986 V. Okhonin (former PhD student) published a new algorithm for training neural networks (for synchronized and non-synchronized networks, for discrete and continuous time, for systems with delays in time, and for many other cases). The central idea was the flexible use of duality (it is a rather usual step in optimization methods). (At the same time, Rumelhart D.E., Hinton G.E., Williams R.J. published a particular case of this algorithm that became famous under the name “back propagation of errors”.) For several years we tried to make the training algorithms faster, and network skills more stable. During an interval of fifteen years (1987-2002) we developed a generalized technology of extraction of explicit knowledge from data. This technology was implemented in a series of software libraries and allowed us to create dozens of knowledge-based expert systems in medical and technical diagnosis, ecology and other fields.
On the base of this approach, the Russian Close Corporation "Applied Radiophysics - Security Systems" developed neural network-based security systems (1997 – 2003). This Russian system "Voron" was the laureate of the international exhibition "Frontier-2000" (see http://etic-m.narod.ru/company.htm, http://www.grand-prix.ru/catalogue/perimeter/voron/solution/ (in Russian).
The results were summarized
in several monographs, 16 PhD theses were submitted, and 3 scientists prepared
Doctor of Science degrees. The developed software is in widespread use in the
5. Physical Kinetics and Invariant Manifolds (1977-present)
The concept of the slow invariant manifold is recognized as the central idea underpinning a transition from micro to macro and model reduction in kinetic theories. We developed constructive methods of invariant manifolds for model reduction in physical and chemical kinetics. The physical problem of a reduced description is studied in the most general form as a problem of constructing the slow invariant manifold. A collection of methods to derive analytically and to compute numerically the slow invariant manifold is elaborated. Among them, iteration methods based on incomplete linearization, relaxation methods and the method of invariant grids have been developed. The systematic use of thermodynamic structures and of the quasi-chemical representation allows us to construct approximations which are consistent with physical restrictions at each step.
There are many examples of applications: nonperturbative derivation of physically consistent hydrodynamics from the Boltzmann equation and from reversible dynamics, for Knudsen numbers Kn near one; construction of the moment equations for nonequilibrium media and their dynamical correction in order to gain more accuracy in the description of highly nonequilibrium flows; the kinetic theory of phonons; model reduction in chemical kinetics; derivation and numerical implementation of constitutive equations for polymeric fluids. A review of this direction of work is now published in Physics Reports.
A new approach to the lattice Boltzmann method is developed. Beginning from thermodynamic considerations, the LBM can be recognised as a discrete dynamical system generated by entropic involution and free-flight and the stability analysis is more natural. We solve the stability problem of the LBM on the basis of this thermodynamic point of view. The main instability mechanisms are identified. The simplest and most effective receipt for stabilisation adds no artificial dissipation, preserves the second-order accuracy of the method, and prescribes coupled steps: to start from a local equilibrium, then, after free-flight, perform the overrelaxation collision, and after a second free-flight step go to new local equilibrium. Two other prescriptions (“salvation rules”) add some artificial dissipation locally and prevent the system from loss of positivity and local blow-up.
6. Bioinformatics and Geometry of Genome (1990-now)
Is it possible to study the genetic text on the same way as A. Kolmogorov studied poetry? Is there a footprint of biological sense in statistical features of the genome? This question needs to be carefully solved. The result may be positive or negative. Nevertheless, we should study this problem. We have investigated a numbe of questions in this direction.
Some positive results have been obtained and published during the past fourteen years. In particular, the clear seven-cluster structure of genome was identified. We studied cluster structure of several genomes in the space of olygomer frequencies. The result: many complete genomic sequences were analyzed, using visualization of tables of triplet counts in a sliding window. The distribution of 64-dimensional vectors of triplet frequencies displays a well-detectable cluster structure. The structure was found to consist of seven clusters, corresponding to protein-coding information in three possible phases in one of the two complementary strands and in the non-coding regions. Awareness of the existence of this structure allows development of methods for the segmentation of sequences into regions with the same coding phase and non-coding regions. This method may be completely unsupervised.