Challenges of machine learning and mathematical modeling

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The article considers the challenges and problems of machine learning that arise in supercomputer mathematical modeling of real-world processes and phenomena. Currently, such modeling has become the main tool for obtaining fundamental and applied knowledge, as well as a condition for a significant increase in labor productivity and gross domestic product. The principles of modern predictive modeling based on high-performance computing, artificial intelligence and big data processing are described. The trends in the development of high-tech mathematical and software within the framework of integrated computing environments are analyzed; the latter imply a flexible expansion of the composition of the studied models and applied algorithms, the effective use of external products, adaptation to the evolution of computer platforms focused on a long life cycle. The methodology of machine learning based on the technological cycle is presented, which includes the formation and modification of models, the implementation of a computational experiment with the solution of direct and inverse problems, analysis of the results and decision-making on optimizing activities to achieve the goals.

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作者简介

V. Ilyin

Institute of Computational Mathematics and Mathematical Geophysics SB RAS; Novosibirsk State Technological University

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Email: ilin@sscc.ru

доктор физико-математических наук, главный научный сотрудник лаборатории вычислительной физики

俄罗斯联邦, Novosibirsk; Novosibirsk

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