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    kanav99 on gh-pages

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    KirillZubov closed #240
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  • 03:26
    ChrisRackauckas commented #719
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  • 01:01

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    christopher-dG on 173ec862-done

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  • 00:43
  • Jan 22 22:27
    anandijain opened #719
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[slack] <chrisrackauckas> or use MTK to transform it
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[slack] <Arno Strouwen> The surrogate optimizer page is missing a few examples with all the different NewOptimizatonType names in them. I can guess that Expected Improvement is going to be EI(), but it could be nice to add them.
http://surrogates.sciml.ai/dev/optimizations/
Also the formatting is not very uniform over the different methods, similarly so for /dev/surrogate/ and /dev/sample/ .
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[slack] <ludoro> Thanks for the comment! Will have a pr ready in the weekend 🙂
[slack] <ludoro> Would you care to open an issue with your suggestions?
[slack] <Arno Strouwen> I will do that. But I also have some other questions:
[slack] <Arno Strouwen> If I remember correctly there used to be an example in the docs linking another kriging library, where the surrogate was fitted using maximum likelihood. I can no longer find this example, I found the ML estimation much more robust than tweaking the kriging hyperparameters in Surrogates.jl. The main page still references this functionality.
[slack] <ludoro> Oh that’s strange, i think you are referring to Stheno.jl though
[slack] <Arno Strouwen> Yes I think it was Stheno. There is still one reference to that in the docs, but nothing more.
[slack] <ludoro> On the Hyperparameter optimization, I plan on having a tutorial on that showing how to use surrogates with some common libraries.
[slack] <ludoro> Thanks for your suggestions!
[slack] <Arno Strouwen> One more question: Any suggestions which surrogate and Bayesian optimizer is best when you have a noisy objective, because it involves (Markov-chain) Monte-Carlo integration?
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[slack] <ludoro> Mmm, take a look at Kriging for sure. Polynomial expansion might be a good choice as well.
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[slack] <Arno Strouwen> I made an issue.
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[slack] <chrisrackauckas> In about 45 minutes (8:30pm EST) I'm doing a quick seminar talk which will discuss some of the stuff with universal differential equations and adjoints of differential equations. If anyone wants to join you can find a zoom link here: https://www.cmu.edu/aced/sciML.html
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[slack] <briochemc> What's the correct way to check if a function is inplace? I.e., in
```julia> foo(du, u, p, t) = "in place"
foo (generic function with 1 method)

julia> ODEFunction(foo)
(::ODEFunction{true,typeof(foo),LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing}) (generic function with 7 methods)`` how does ODEFunction assigniip = true`? Not sure where to look for the code that does that 😅

[slack] <briochemc> I'm guessing it checks nargs, but I want to be sure how it's supposed to be done 🙂
[slack] <briochemc> (also a nargs search on DiffEqBase gave me no results 🤔)
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[slack] <briochemc> Oh there's a numargs !
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[slack] <briochemc> Oh so DiffEqBase uses length(m.sig.parameters) instead of m.nargs . I guess I don't understand why but that's ok 🙂 I can just use DiffEq's isinplace I think
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[slack] <OJASV Kamal> When I multiply f with d^2(f)/dx^2 in the first equation it throws the error that an ode system can have only one independent variable. But f is a dependent variable
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[slack] <dalarev> Hey everybody, before getting too deep into it, IterativeSolvers: gmres doesn't support overdetermined systems (i.e., non-square A?)
[slack] <chrisrackauckas> it should?
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[slack] <mschauer> Try with explicit x0?
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[slack] <dalarev> An explicit output, you mean?: https://files.slack.com/files-pri/T68168MUP-F01FDSPFD52/download/image.png
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[slack] <mschauer> I thought gmres!(x, A, y), but that also doesnt work
[slack] <mschauer> I looked at idrs and it would require some thinking to tell if this is legal for overdetermined systems for example
[slack] <dalarev> A\b works, but I am benchmarking different methods.
[slack] <mschauer> A\b does something perhaps overly smart and switches the solver depending on the dimensions i believe
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[slack] <dalarev> https://discourse.julialang.org/t/efficient-way-of-doing-linear-regression/31232/33 post tells me it's probably the most accurate, but my A matrix comprises derivative operators, so I'm also looking at DiffEqOperators.
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[slack] <mschauer> Solve the normal equations:
gmres(A'*A, A'*y)
[slack] <mschauer> and then perhaps with a solver which uses the Hermitian structure… ah, you just said so much
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[slack] <mschauer> use conjugate gradient descent on the normal equations, tells me a helpful voice
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[slack] <Jose Daniel Lara> @chrisrackauckas is there a more updated version of Benchmarks vs Matlab solvers from DiffEq?
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[slack] <dalarev> From the discourse post I linked to, it sounds like doing A'A could square my errors, and I think for my application accuracy is most important.
[slack] <mschauer> Well, can you do QR? Then do it. I assumed that you use an iterative solver because you must
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[slack] <yingbo_ma> Seems like we are also getting

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[slack] <David Millard> Hi, I have a second order ODE in a classical mechanics context. Is SecondOrderODEProblem still supported? I had a hard time understand what inputs the dynamics function should take. Unfortunately reading https://github.com/SciML/DiffEqBase.jl/blob/864c379ead274243acdd965bd8942d1da1deb3bb/test/problem_creation_tests.jl#L51 didn't shed a lot of light for me. I'm not sure what the relationship between u and v are, and the docstring args from the SecondOrderODEProblem constructor seems flipped compared to its implementation. Thanks for any information!
[slack] <chrisrackauckas> @David Millard https://tutorials.sciml.ai/html/models/01-classical_physics.html should help
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[slack] <David Millard> That's exactly what I was missing. Thank you!
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[slack] <jonas.isensee> Hi,
what's the best way to implement a system where I have states of the form u0 = (rand(3), rand(3,3))
I know that I can just work with u0 = rand(3,4) but that makes the eoms rather ugly. The above doesn't work though
[slack] <chrisrackauckas> RecursiveArrayTools.ArrayPartition
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[slack] <jonas.isensee> Thanks you, that seems to work.
Not sure that is much better for my particular usecase though.
I want to work with the matrix directly and
u.x[2] is probably not much better than`@view u[:,2:4]`
at the cost of introducing another abstraction layer.

(relevant since this is for teaching...)

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[slack] <chrisrackauckas> views are probably a nice canonical way to do it if it's simple enough
[slack] <chrisrackauckas> LabelledArrays.jl is another.
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[slack] <jonas.isensee> Ha, I didn't know that package! This looks really cool.

For the sake of the students I will probably still stick to regular views
but I'll see if I can incorporate that into my normal work 🙂

[slack] <jonas.isensee> Thanks for your help
[slack] <chrisrackauckas> no problem