New tools to solve PDEs in Julia with Gridap.jl | Francesc Verdugo et al | JuliaCon2021

This talk was given as part of JuliaCon 2021. Abstract: In this talk, we explore the novel capabilities of Gridap to solve Partial Differential Equations (PDEs) in Julia. This includes new features like a high-level API to write the PDE weak form with a syntax almost identical to the math notation, support for automatic differentiation, and simulation of PDEs on manifolds and domains of mixed geometrical dimensions. We will showcase these techniques with representative applications and performance comparisons against codes implemented in C/C++. For more info on the Julia Programming Language, follow us on Twitter:   / julialanguage   and consider sponsoring us on GitHub: https://github.com/sponsors/JuliaLang Contents 0:00 Welcome! 0:25 Some PDEs Solved With Gridap In 2021 0:39 Outline 1:00 New User API 1:50 How Efficient Is Gridap? 2:52 Simulating Complex Geometries In Gridap 5:10 Simulating Real-World Applications In Gridap S/O to https://github.com/cmdenis 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...