2020-07-31 –, BoF
Dynamical modeling is arguably one of the biggest strengths of the Julia programming language. With DifferentialEquations.jl, DynamicalSystems.jl, RigidBodyDynamics.jl, ModelingToolkit.jl, DiffEqBiological.jl, Pumas.jl, etc. (the list keeps going), there are many state-of-the-art award winning projects. However, ,what's missing? What's next? Let's discuss and figure out some next steps. Join the BoF channel on Discord.
There are many directions that we can explore. What should we as a community be prioritizing? I think it would be good to hear from users what they think is "complete", and what gaps commonly show up. There's many topics this discussion can go towards:
1) Improved modeling tools and DSLs (ModelingToolkit)
2) Automated PDE solving
3) Targeting alternative hardware (exporting models for embedded systems)
4) New domains: integro-differential equations, fractional differential equations
5) Geometric methods and DAEs
6) Parallelism (MPI, GPUs, alternative acceleration hardware)
7) Connections: mixing ApproxFun.jl and FEM packages with DifferentialEquations.jl, etc.
8) Continuing to improve benchmarking
9) Accessibility, tutorials, blog posts, etc.
10) Whatever else comes to mind!
Christopher Rackauckas is an Applied Mathematics Instructor at the Massachusetts Institute of Technology and a Senior Research Analyst at University of Maryland, Baltimore, School of Pharmacy in the Center for Translational Medicine. Chris's research is focused on numerical differential equations and scientific machine learning with applications from climate to biological modeling. He is the developer of over many core numerical packages for the Julia programming language, including DifferentialEquations.jl for which he won the inaugural Julia community prize, and the Pumas.jl for pharmaceutical modeling and simulation. He is the lead developer for the SciML Open Source Scientific Machine Learning software organization, along with its packages like DiffEqFlux.jl and NeuralPDE.jl