07-31, 18:00–19:30 (UTC), Red Track
Chairs:
Jürgen Fuhrmann (Weierstrass Institute Berlin),
Petr Krysl (UCSD)
The talks at the minisymposium present several packages devoted to the solution of partial differential equations based on various approaches to space discretization including finite element, finite volume, boundary element and neural networks.
During the conference chat, developments of the PDE simulation infrastructure shall be discussed.
Julia with its near optimal scalar performance, built-in multithreading, multiprocessing, and packages for GPU computing in combination with its generic programming facilities provides a new opportunities to implement high- performing and easy to uses packages for PDE solution.
On the other hand, it is likely that the full potential of Julia with respect to this problem class has not been reached yet. The ecosystem of packages and the language itself have barely matured.
We propose a minisymposium whose contributors present their Julia packages connected with the solution of partial differential equations and systems thereof. The talks shall appeal to a broader public.
Updated July, 26:
The following pre-recorded talks will be given:
- Petr Krysl: "Julia for PDEs: Come for the speed, stay for ... much more" 10min
- Jürgen Fuhrmann: "VoronoiFVM.jl: Finite Volume Methods for Nonlinear Multiphysics Problems" 17min
- Kristof Cools: 'BEAST.jl - Minisymposium on Partial Differential Equations" 19min
- Kirill Zubov: "NeuralPDE.jl: Physics Informed Neural Networks for Automated PDE solving" 15min
- Michael Reed: "Grassmann.jl - Minisymposium on Partial Differential Equations" 17min
During the remaining time, we will discuss the state of Julia concerning the solution of partial differential equations:
- What are the pieces that are in your opinion missing from the Julia for PDE ecosystem?
- What would make the ecosystem easier to use for novice users?
- What would make it easier to collaborate?
- What do you see as a role for the Julia PDE organization?