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ChrisRackauckas/DifferentialEquations.jl

Julia package for solving differential equations

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03:17

ChrisRackauckas commented #869
May 27 14:34

grahamrow commented #863
May 27 13:35

grahamrow commented #863
May 27 13:28

grahamrow commented #863
May 27 12:10

mantasjk opened #869
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InterdisciplinaryPhysicsTeam edited #868
May 27 01:40

ChrisRackauckas commented #863
May 27 01:35

grahamrow commented #863
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grahamrow commented #863
May 26 21:59

InterdisciplinaryPhysicsTeam edited #868
May 26 21:59

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InterdisciplinaryPhysicsTeam edited #868
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InterdisciplinaryPhysicsTeam opened #868
May 22 23:53

acoh64 reopened #866
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ChrisRackauckas opened #867
May 22 21:43

ChrisRackauckas closed #490
May 22 21:43

ChrisRackauckas commented #490
May 22 14:30

YingboMa commented #866

Christopher Rackauckas

@ChrisRackauckas

None of them really are.

I mean, you can find a bunch of articles on it.

For example: http://www.sciencedirect.com/science/article/pii/089571779490099X

Paweł Biernat

@pwl

have you tested it?

Christopher Rackauckas

@ChrisRackauckas

There are a bunch of articles which I think tend to show that Waveform Relaxation "kind of" came out on top of the other parallel methods (parallel extrapolation, special Runge-Kutta methods)

But it's supposedly a little difficult to program.

That's why I want to try it out.

I know Hairer has parallel extrapolation in ODEX

so there's no reason to code that

Paweł Biernat

@pwl

thanks!

Christopher Rackauckas

@ChrisRackauckas

The special Runge-Kutta methods you just need to use threading. They just turn it into solving Ax=b each step in some simplified way.

Christopher Rackauckas

@ChrisRackauckas

@pwl This is a good SE post for explaining the difference between the major groups., and here is another reference for the differences . We might think about writing a wrapper to PFASST. Actually I think it we wrap enough of them or put enough of these algorithms under the same roof we might be able to get a review article out of it.

In the end I am looking to try a few of them since I am interesting in which ones may be useful for stochastic differential equations.

But I don't really think that any program puts all of these together already, so this is something to be done.

While I'm at it, the other ODE methods I am looking into implementing are super high order RKs.

Let me know if you plan on implementing any of these so we don't double up!

Paweł Biernat

@pwl

have you looked at high order Taylor methods?

Christopher Rackauckas

@ChrisRackauckas

I mean, I know how they're derived.

Paweł Biernat

@pwl

There was some package on github (now removed), that implemented arbitrary order Taylor methods, which I hooked up to ODE to test the backend API and the method, they worked!

Christopher Rackauckas

@ChrisRackauckas

Did you have to provide the derivatives?

Paweł Biernat

@pwl

ForwardDiff does it for you

Christopher Rackauckas

@ChrisRackauckas

But for really high derivatives, that doesn't give more overhead?

Paweł Biernat

@pwl

it does, but on the other hand you can make longer stepsizes

Christopher Rackauckas

@ChrisRackauckas

Depending on the function being "nice"

Paweł Biernat

@pwl

so it sort of balances out

Christopher Rackauckas

@ChrisRackauckas

With automatic differentiation... that's interesting. I'd like to test that out vs other methods.

Paweł Biernat

@pwl

I tested it with a hyperbolic PDE that I work on and it worked as quick as RK

Christopher Rackauckas

@ChrisRackauckas

I thought of using symbolic differentiation before with Taylor methods.

Paweł Biernat

@pwl

at least the same order of magnitude

the fun thing with this method is that you can prescribe an error much lower then the machine epsilon

like reltol 1e-30 with Float64

Christopher Rackauckas

@ChrisRackauckas

Probably scales better than extrapolation.

Paweł Biernat

@pwl

but this is a niche application

Christopher Rackauckas

@ChrisRackauckas

Well you'd need to go to other numbers to avoid weird truncation error problems at that point.

But a battle between high-order extrapolation, RK16, and 16th order Taylor series via automatic differentiation.

I'd want to try that out just for fun.

People in astrodynamics may find that useful.

Mauro

@mauro3

@PerezHz looks into taylor methods in his PhD with Benet & Sanders: https://github.com/JuliaLang/ODE.jl/issues/91#issue-139761284

Christopher Rackauckas

@ChrisRackauckas

But @pwl said the package is no longer available?

Paweł Biernat

@pwl

unfortunately the package disappeared

I still have a hard copy on my hd

Christopher Rackauckas

@ChrisRackauckas

Please share :smile:

It will have to be updated for sure.

I know ForwardDiff no longer exports the derivative functions, so someone will have to go in and change those kinds of things.

Paweł Biernat

@pwl

I don't know how @PerezHz would feel about it, maybe you should write him first?

Christopher Rackauckas

@ChrisRackauckas

Okay, I'll email him.

Thanks for the heads up on this

Paweł Biernat

@pwl

if you won't be able to reach him you can try to write @lbenet, he will know what the status of the package is

I think he is his advisor, but I'm not so sure about it now:-)

Jorge Pérez

@PerezHz

Hi everyone! @ChrisRackauckas thanks for contacting us, glad to know you're interested in what we've been working on! :smile: Yes, we did take it down, we're re-organizing it (it's pretty much mixed with some celestial mechanics stuff) and planning to re-upload the generic parts of the code

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