Перегляд за Автор "Novotarskyi, Mykhailo"

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    Simulation of fluid motion in complex closed surfaces using a lattice Boltzmann model
    (National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 2022) Kuzmych, Valentyn; Novotarskyi, Mykhailo
    CFD (computational fluid dynamics) modeling is used to determine the distribution of pressure, velocity, and other movement parameters of liquids or gases. Simulation of a fluid flow in a complex closed surface has become demandable in many scientific, medical, and industrial areas. The lattice Boltzmann model is an efficient numerical scheme for modeling fluid flows. In this paper, we investigate nonstationary hydrodynamic processes in closed surfaces using the Boltzmann lattice model to simulate fluid flow in the human stomach.
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    USAK method for the reinforcement learning
    (National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 2020) Novotarskyi, Mykhailo; Kuzmich, Valentin
    In the field of reinforcement learning, tabular methods have become widespread. There are many important scientific results, which significantly improve their performance in specific applications. However, the application of tabular methods is limited due to the large amount of resources required to store value functions in tabular form under high-dimensional state spaces. A natural solution to the memory problem is to use parameterized function approximations. However, conventional approaches to function approximations, in most cases, have ceased to give the desired result of memory reduction in solving real world problems. This fact became the basis for the application of new approaches, one of which is the use of Sparse Distributed Memory (SDM) based on Kanerva coding. A further development of this direction was the method of Similarity-Aware Kanerva (SAK). In this paper, a modification of the SAK method is proposed, the Uniform Similarity-Aware Kanerva (USAK) method, which is based on the uniform distribution of prototypes in the state space. This approach has reduced the use of RAM required to store prototypes. In addition, reducing the receptive distance of each of the prototypes made it possible to increase the learning speed by reducing the number of calculations in the linear approximator.