Search results for: large deflection, buckling, nonlocal elasticity theory, graphene sheets

• Theory of Elasticity and Plasticity

  e-Learning Courses

  • R. Sauer
  • I. Lubowiecka

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

• Theory of Elasticity and Plasticity 2023

  e-Learning Courses

  • R. Sauer
  • I. Lubowiecka

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

• Theory of Elasticity and Plasticity 2024

  e-Learning Courses

  • 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

  e-Learning Courses

  • 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

  e-Learning Courses

  • 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

  e-Learning Courses

  • 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

  e-Learning Courses

  • 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...