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My defunct startup Staffjoy operated from 2015 to 2017 and was an early production user of Julia. We open-sourced some of the code here: https://github.com/staf

My defunct startup Staffjoy operated from 2015 to 2017 and was an early production user of Julia. We open-sourced some of the code here: https://github.com/staffjoy https://github.com/staffjoy I haven't kept up with the ecosystem - I'm curious how things have improved in the last 5 year!

leephillips 4y ago
Probably the most obvious improvements for most users are the big growth in the package ecosystem, with some science and math packages becoming the best available in any language (DifferentialEquations.jl, for example) and the drastic improvement in interactivity: the REPL starts instantly now, and most packages compile much faster. This will get even better in the next major release.
- Q6T46nT668w6i3m 4y ago
  I’m curious (I’m planning on designing an ODE/PDE package relatively soon), what makes DifferentialEquations.jl the best available?
  - logankilpatrick 4y ago
    The fact that Chris R and the whole SciML ecosystem are using / building on it. Lots of resources going into it and usage in real world places like Pfizer and Moderna.
  - leephillips 4y ago
    This lists some of its unique abilities: https://docs.sciml.ai/DiffEqDocs/stable/ https://docs.sciml.ai/DiffEqDocs/stable/ The routines are sufficiently generic, with regard to Julia’s type system, to allow the solvers to automatically compose with other packages and to seamlessly use types other than Numbers. For example, instead of handling just functions Number→Number, you can define your ODE in terms of quantities with physical dimensions, uncertainties, quaternions, etc., and it will just work (for example, propagating uncertainties correctly to the solution¹). Recent developments involve research into the automated selection of solution routines based on the properties of the ODE, something that seems really next-level to me. [1] https://lwn.net/Articles/834571/ https://lwn.net/Articles/834571/
  - adgjlsfhk1 4y ago
    My best answer for you is https://docs.sciml.ai/DiffEqDocs/stable/solvers/ode_solve/#Full-List-of-Methods https://docs.sciml.ai/DiffEqDocs/stable/solvers/ode_solve/#F.... It's the list of the ~250 different solvers you can choose from (note that most of these have a bunch of customization options). DifferentialEquations.jl has heuristics that do a pretty good job of picking a good algorithm on it's own, but it also lets you test every algorithm you've heard of (and a bunch you haven't) to find the one that works perfectly for your problem.

leephillips 4y ago

Probably the most obvious improvements for most users are the big growth in the package ecosystem, with some science and math packages becoming the best available in any language (DifferentialEquations.jl, for example) and the drastic improvement in interactivity: the REPL starts instantly now, and most packages compile much faster. This will get even better in the next major release.

Q6T46nT668w6i3m 4y ago
I’m curious (I’m planning on designing an ODE/PDE package relatively soon), what makes DifferentialEquations.jl the best available?
- logankilpatrick 4y ago
  The fact that Chris R and the whole SciML ecosystem are using / building on it. Lots of resources going into it and usage in real world places like Pfizer and Moderna.
- leephillips 4y ago
  This lists some of its unique abilities: https://docs.sciml.ai/DiffEqDocs/stable/ https://docs.sciml.ai/DiffEqDocs/stable/ The routines are sufficiently generic, with regard to Julia’s type system, to allow the solvers to automatically compose with other packages and to seamlessly use types other than Numbers. For example, instead of handling just functions Number→Number, you can define your ODE in terms of quantities with physical dimensions, uncertainties, quaternions, etc., and it will just work (for example, propagating uncertainties correctly to the solution¹). Recent developments involve research into the automated selection of solution routines based on the properties of the ODE, something that seems really next-level to me. [1] https://lwn.net/Articles/834571/ https://lwn.net/Articles/834571/
- adgjlsfhk1 4y ago
  My best answer for you is https://docs.sciml.ai/DiffEqDocs/stable/solvers/ode_solve/#Full-List-of-Methods https://docs.sciml.ai/DiffEqDocs/stable/solvers/ode_solve/#F.... It's the list of the ~250 different solvers you can choose from (note that most of these have a bunch of customization options). DifferentialEquations.jl has heuristics that do a pretty good job of picking a good algorithm on it's own, but it also lets you test every algorithm you've heard of (and a bunch you haven't) to find the one that works perfectly for your problem.