Обратные задачи моделирования на основе регуляризации и распределенных вычислений в среде Everest
Problem in the Modeling on the Basis of Regularization and Distributed Computing in the Everest Environment
A method for estimating mathematical models of physical spatial phenomena is presented. Estimating is based on the series of experimental data. The objective function in the inverse optimization problem of identification of model parameters includes a regularizing term with unknown weight coefficients for the 2nd derivatives of the spatial function describing the phenomenon. Successive cross-validation procedure is used to choose values of weight coefficients. This cross-validation consists in approximation of one subset of experimental data by processing of a complementary subset. The better accuracy of the “crossapproximation”, the better set of weight coefficients. Choosing direction of the possible improvement requires solving a number of subsidiary optimization problems. For that it is proposed to use distributed computing environment of optimization services deployed via Everest toolkit.