Automatic Differentiation for Solid Mechanics in Julia | Andrea Vigliotti | JuliaCon 2022 скачать в хорошем качестве

Automatic Differentiation for Solid Mechanics in Julia | Andrea Vigliotti | JuliaCon 2022

Automatic Differentiation (AD) is widely applied in many different fields of computer science and engineering to accurately evaluate derivatives of functions expressed in a computer programming language. In this talk we illustrate the use of AD for the solution of Finite Elements (FE) problems with special emphasis on solid mechanics. For more info on the Julia Programming Language, follow us on Twitter:   / julialanguage   and consider sponsoring us on GitHub: https://github.com/sponsors/JuliaLang Resources AD4SM.jl package repository: https://github.com/avigliotti/AD4SM.jl Slides of presentation and additional examples: https://github.com/avigliotti/AD4SM_e... Andrea Vigliotti & Ferdinando Auricchio, "Automatic Differentiation for Solid Mechanics". Arch Computat Methods Eng 28, 875–895 (2021): https://doi.org/10.1007/s11831-019-09... Contents 0:10 Opening and introduction 0:30 Why AD for solid Mechanics? 1:50 One example, the rod element 2:50 Why Julia? 3:24 AD4SM.jl 4:00 How a second order forward mode AD System works 6:46 Examples of simulation results S/O to https://github.com/avigliotti for the video timestamps! Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/JuliaCommunity/You... Interested in improving the auto generated captions? Get involved here: https://github.com/JuliaCommunity/You...