Wyniki wyszukiwania dla: large deflection, buckling, nonlocal elasticity theory, graphene sheets - MOST Wiedzy

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Wyniki wyszukiwania dla: large deflection, buckling, nonlocal elasticity theory, graphene sheets

Wyniki wyszukiwania dla: large deflection, buckling, nonlocal elasticity theory, graphene sheets

  • Theory of Elasticity and Plasticity

    Kursy Online
    • R. Sauer
    • I. Lubowiecka

    This course discusses the general theory of elastic and plastic material behavior of solids.

  • Theory of Elasticity and Plasticity 2023

    Kursy Online
    • R. Sauer
    • I. Lubowiecka

    This course discusses the general theory of elastic and plastic material behavior of solids.

  • Theory of Elasticity and Plasticity 2024

    Kursy Online
    • B. Łazorczyk
    • R. Sauer
    • M. Skowronek

    This course discusses the general theory of elastic and plastic material behavior of solids.

  • Theory of Elasticity and Plasticity - Civil Engineering, sem. I

    Kursy Online
    • M. Skowronek

    Preliminaries in Solid Body Mechanics focused on 2D and 3D engineering structures, in analytical approach

  • Technical Mechanics 2 (PG_00049753), I stop, En, [W,C], zima 22/23

    Kursy Online
    • B. Rozmarynowski

    Lectures and tutorials devoted to basic issues of Strength of Materials (stress and strain states, the Hook's law, geometrical properties of an area, bending, shearing, torsion, deflection lines  of beams, buckling and yielding criteria).

  • Technical Mechanics 2

    Kursy Online
    • M. Kahsin

    The aims of lecture is to provide basic knowledge of strength of materials and its exploitation in assessmentof structural stress and deformation. Subject contents: 1) Introduction, 2) Stress-strain relations, physical interpretation, 3) Axial loading of rods, 4) Moments ofinertia, 5) Bending of beams, 6) Beams line of deflection, 7) Shearing, 8) Torsion, 9) Complex stress yieldcriterion, 10) linear buckling of column.

  • Deep neural networks for data analysis 24/25

    Kursy Online
    • J. Cychnerski
    • K. Draszawka

    This course covers introduction to supervised machine learning, construction of basic artificial deep neural networks (DNNs) and basic training algorithms, as well as the overview of popular DNNs architectures (convolutional networks, recurrent networks, transformers). The course introduces students to popular regularization techniques for deep models. Besides theory, large part of the course is the project in which students apply...