I was bit by trying to figure out of how to combine unit of measurements with other numerical computations. Ultimately a lot of the features look great on paper, but once you start using them, I only ever was able to produce an ungodly mess instead of what I could accomplish in Python in roughly the same time. Everything that goes beyond what Matlab does, sometimes looks great on paper but is not very pleasant to use / sometimes badly broken unfortunately. That being said I work in an area of scientific research where Julia or more specifically DifferentialEquations.jl would seem to walk away with the win, but I find myself searching for alternatives implemented in Jax.
I would still think most of this is my failings, but it is also extraordinarily hard to figure out what is going wrong.
Anything other than units? I'd be curious to know. Unitful.jl is something which I think is completely the wrong architecture (it violates many standard assumptions about arrays when used in arrays) so that's a somewhat special case (and I plan to create a new units library to completely remove uses of Unitful).
I greatly appreciate your work, by the way, I just have not been able to make use of it effectively. Some of the problems revolve around the question of how to best proceed in a situation, where you have a high dimensional state space, but it is naturally partitioned in some way. There are several solutions for this (SubArray etc.), but the burden is entirely put on the user. I had the impression that there was a tension between what I would have preferred to write and what I could easily write as soon as I attempted to generalize from the examples that I could find. With JAX the corresponding libraries operate on Pytrees and as a user you can specify equations pretty naturally and easily without much fuss. If you want to use XLA, MPI, CUDA in Julia it typically is the same, in theory it should be possible to make things work in practice I have struggled tremendously to do anything productive.
When I looked at the adjoint event handling code last, I couldn’t figure out in the implementation, whether the general case was handled correctly, especially since parts of it still seemed in flux. Writing similar code in JAX leaves close to no room for interpretation that the code is correct. I am sure most of it is down to familiarity. But since ultimately I want to do ML relared things, right now JAX and related libraries ties up things that are there much more neatly even though overall SciML implements a more comprehensive set of techniques. I am still closely following the work around it especially in the area I am interested in and have some prototypes written in it, but it just hasn’t clicked yet.
I highly recommend taking a look at George Hart's work on linear algebra: http://georgehart.com/research/multanal.html , although I do think he misses the point (or at least insufficiently emphasizes) that you almost never want to work with linear maps that cannot be described by a set of units on each axis that are multiplied together to get the entries.
I am curios, what are those many standard assumptions about arrays that Unitful violates. There does not seem to be any space left for alternatives as representing each element with struct:
> struct Element{T, Unit} <: Number
> value::T
> end
You may already know of it, but if you want differential-equations-in-JAX then allow me to quickly advertise Diffrax: https://github.com/patrick-kidger/diffrax (of which I am the author, disclaimer).
I would still think most of this is my failings, but it is also extraordinarily hard to figure out what is going wrong.