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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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On continuity equations in infinite dimensions with non-Gaussian reference measure

Journal of Functional Analysis. 2014. Vol. 266. No. 7. P. 4490–4537.
Kolesnikov A., Roeckner M.

Let γ be a Gaussian measure on a locally convex space X and H be the corresponding Cameron-Martin space. It has been recently shown by L. Ambrosio and A. Figalli that the linear first-order transportational PDE  on X admits a weak solution  under broad assumptions.  Applying transportation of measures via triangular maps we prove a similar result for a large class of non-Gaussian probability measures ν on $\R^{\infty}$, under the main assumption of integrability of logarithmic derivativesof v. We also show uniqueness of the solution for a wide class of measures. This class includes uniformly log-concave Gibbs measures and certain product measures. measures.

Research target: Mathematics
Language: English
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Keywords: Gaussian measuresгауссовские мерыcontinuity equationуравнение непрерывностиmeasures on infinite-dimensional spacestriangular transformation of mappingsмеры на бесконечномерных пространствахтреугольные преобразования мер
Publication based on the results of:
Оптимальная транспортировка мер и ее приложения (2012)
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