Libbum

It is surprisingly good! Not clear if you can go without a manual step where you need to change the likes of SPEAKER_00 to Bob and SPEAKER_01 to Sarah, but I've not had it mess up on me at all transcribing 2 hour long conversations between 6 people.

You may have been reading a different version of the documentation. Documenter.jl outputs are dependent on the code actually running (and it's the package DifferentialEquations.jl uses to build its docs).

An additional thing to understand here is that PFAS is not a new boundary at all in the context of the planetary boundaries framework - they fall under the already defined category of Novel Entities.

Since that's a catch all category for many things (nuclear waste, other synthetic chemicals, there's even debate with the original authors of the framework if Artificial Intelligence should be considered), the boundary value itself is not currently defined.

One school of thought is that the boundary value for this category should be zero - as any synthetic substance is more than nature generates.

Regardless, papers like this one are helpful to piece together all of the novel entities research amd get a better picture of how this boundary interacts with the rest of the Earth System.

That's interesting! Care to share your fail2ban config for this?

I certainly agree with the NNs are used as hammers point. Until coming across the UODE concept I was of the opinion they were more parlour trick than anything useful. Here though, I could see some validity.

These comments are appreciated - I think a discussion like this is lacking in the SciML docs (or at least not visible enough). Will have a chat with some of the devs and see if there's something we can add.

I'm not entirely confident in answering that directly, so perhaps you can check my reasoning here.

If F is completely unknown, perhaps you start training with a 10 dimensional polynomial basis. What is the (computational) cost of obtaining your solution? Once you have it, will this polynomial accurately represent your system in any real world manner? Perhaps higher order parameters are needed to approximate trigonometric functions - are you able to easily add such functions to your training basis? If not - then your basis could be too restrictive to provide you with a minimal implementation of your control variable.

It looks like you work with this stuff far more than I have, so perhaps that's not an adequate answer.

Another way to look at this though: If you only wanted to characterise your system with polynomials, UODEs + SINDy can do this for you - the NN is simply the optimisation method that's in place of any other optimisation algorithm.

I mostly read scientific papers. Quite a good number of them are two column. Haven't come across k2pdfopt before - I'll check it out, thanks!

Version 1. But as I say, the hardware is good. Version 2 will have even better hardware specs, but the operating system I suspect will be the same.