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Earlier, a method for constructing an initial approximation for solving the inverse problem of acoustics by a gradient method based on a convolutional neural network trained to predict the distribution of velocities in a medium from wave response was proposed [9]. It was shown that the neural network trained on responses from simple layered media can be successfully used for solving the inverse problem for a significantly more complex model. In this paper, we present algorithms for processing data about epidemics and an example of applying a neural network for modeling the propagation of COVID-19 in Novosibirsk region (Russia) based only on data. A neural network NN-COVID-19 that uses data about the epidemics is constructed. It is shown that this neural network predicts the propagation of COVID-19 for five days by an order of magnitude better than SEIR-HCD. When a new variant (Omicron) appeared, this neural network was able to predict (after retraining) the propagation of the epidemics more accurately. Note that the proposed neural network uses not only epidemiological data but also social ones (such as holidays, restrictive measures, etc.). The proposed approach makes it possible to refine mathematical models. A comparison of the curves constructed by SEIR-HCD model and by the neural network shows that the plots of solutions of the direct problem almost coincide with the plots constructed by the neural network. This helps refine coefficients of the differential model. [ABSTRACT FROM AUTHOR]

An algorithm has been developed for numerically solving the source determination problem in the model of information dissemination in synthetic online social networks, described by reaction–diffusion-type equations, using additional information about the process at fixed time points. The degree of ill-posedness of the source determination problem for a parabolic equation is studied based on the analysis of singular values of the linearized operator of the inverse problem. The algorithm developed is based on a combination of the tensor optimization method and gradient descent supplemented with the A.N. Tikhonov regularization. Numerical calculations demonstrate the smallest relative error in the reconstructed source obtained by the developed algorithm in comparison with classical approaches. [ABSTRACT FROM AUTHOR]

Mathematical models for transmission dynamics of the novel COVID-2019 coronavirus, an outbreak of which began in December, 2019, in Wuhan are considered. To control the epidemiological situation, it is necessary to develop corresponding mathematical models. Mathematical models of COVID-2019 spread described by systems of nonlinear ordinary differential equations (ODEs) are overviewed. Some of the coefficients and initial data for the ODE systems are unknown or their averaged values are specified. The problem of identifying model parameters is reduced to the minimization of a quadratic objective functional. Since the ODEs are nonlinear, the solution of the inverse epidemiology problems can be nonunique, so approaches for analyzing the identifiability of inverse problems are described. These approaches make it possible to establish which of the unknown parameters (or their combinations) can be uniquely and stably recovered from available additional information. For the minimization problem, methods are presented based on a combination of global techniques (covering methods, nature-like algorithms, multilevel gradient methods) and local techniques (gradient methods and the Nelder–Mead method). [ABSTRACT FROM AUTHOR]

Inverse problems for systems of nonlinear ordinary differential equations are studied. In these problems, the unknown coefficients and initial data must be found given additional information about the solution to the corresponding direct problems; this information is obtained by measurements made at some specified points in time. Examples of inverse immunology and epidemiology problems arising in the analysis of infectious diseases progression, in the study of HIV dynamics, and spread of tuberculosis in highly endemic regions taking treatment into account are discussed. In the case when the solution to the inverse problem is not unique, three approaches to the study of identifiability of mathematical models are considered. A numerical solution algorithm based on the minimization of a quadratic objective functional is proposed. At the first stage, neighborhoods of the global minimizers are found, and gradient methods are used at the second stage. The gradient of the objective functional is calculated by solving the corresponding adjoint problem. Numerical results are discussed. [ABSTRACT FROM AUTHOR]

Abstract: A numerical algorithm is proposed to solve the source reconstruction problem for a system of nonlinear shallow-water equations using the dynamics of water surface perturbation measured at a finite number of spatial points and/or over a part of the surface at a fixed time. The combined inverse problem under study is reduced to the minimization of an objective functional characterizing the quadratic deviation of simulated data from measured data (a misfit function). An explicit expression for the gradient of the misfit function is obtained. The direct and conjugate problems within the framework of shallow-water equations are solved by the finite volume method. The numerical results are analyzed and compared with experimental data. [ABSTRACT FROM AUTHOR]