Быстрые численные методы стохастического моделирования, основанные на вероятностных расширениях

The article studies fast algorithms for numerical modeling of problems with random input data. The approaches under consideration are non-intrusive methods based on probabilistic extensions. They rely on existing numerical methods for solving deterministic problems and use them as solvers. Unlike polynomial chaos methods, in some cases it is possible to avoid exponential growth of the number of operations from the number of input parameters