StochasticDiffEq

Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem

https://github.com/sciml/stochasticdiffeq.jl

Science Score: 54.0%

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Low similarity (9.9%) to scientific vocabulary

Keywords

adaptive differential-equations differentialequations hacktoberfest ito noise random random-differential-equations rde rode scientific-machine-learning sciml sde sode solvers stochastic stochastic-differential-equations stochastic-processes stratonovich

Keywords from Contributors

pde ode neural-ode dae dde ordinary-differential-equations partial-differential-equations neural-sde differential-algebraic-equations neural-differential-equations

Last synced: 6 months ago · JSON representation ·

Repository

Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem

Basic Info

Host: GitHub
Owner: SciML
License: other
Language: Julia
Default Branch: master
Homepage:
Size: 4.16 MB

Statistics

Stars: 298
Watchers: 12
Forks: 72
Open Issues: 105
Releases: 222

Topics

Created over 9 years ago · Last pushed 6 months ago

Metadata Files

Readme License Citation

README.md

StochasticDiffEq.jl

StochasticDiffEq.jl is a component package in the DifferentialEquations ecosystem. It holds the stochastic differential equations solvers and utilities. While completely independent and usable on its own, users interested in using this functionality should check out DifferentialEquations.jl.

API

StochasticDiffEq.jl is part of the JuliaDiffEq common interface, but can be used independently of DifferentialEquations.jl. The only requirement is that the user passes an StochasticDiffEq.jl algorithm to solve. For example, we can solve the SDE tutorial from the docs using the SRIW1() algorithm:

julia using StochasticDiffEq α=1 β=1 u₀=1/2 f(u, p, t) = α*u g(u, p, t) = β*u dt = 1//2^(4) tspan = (0.0, 1.0) prob = SDEProblem(f, g, u₀, (0.0, 1.0)) sol = solve(prob, SRIW1())

The options for solve are defined in the common solver options page and are thoroughly explained in the ODE tutorial.

That example uses the out-of-place syntax f(u,p,t), while the inplace syntax (more efficient for systems of equations) is shown in the Lorenz example:

```julia function lorenz(du, u, p, t) du[1] = 10.0(u[2]-u[1]) du[2] = u[1](28.0-u[3]) - u[2] du[3] = u[1]u[2] - (8/3)*u[3] end

function σ_lorenz(du, u, p, t) du[1] = 3.0 du[2] = 3.0 du[3] = 3.0 end

probsdelorenz = SDEProblem(lorenz, σlorenz, [1.0, 0.0, 0.0], (0.0, 10.0)) sol = solve(probsde_lorenz) plot(sol, vars = (1, 2, 3)) ```

The problems default to diagonal noise. Non-diagonal noise can be added by setting the noise_prototype:

julia f = (du, u, p, t) -> du.=1.01u g = function (du, u, p, t) du[1, 1] = 0.3u[1] du[1, 2] = 0.6u[1] du[1, 3] = 0.9u[1] du[1, 4] = 0.12u[2] du[2, 1] = 1.2u[1] du[2, 2] = 0.2u[2] du[2, 3] = 0.3u[2] du[2, 4] = 1.8u[2] end prob = SDEProblem(f, g, ones(2), (0.0, 1.0), noise_rate_prototype = zeros(2, 4))

Colored noise can be set using an AbstractNoiseProcess. For example, we can set the underlying noise process to a GeometricBrownianMotionProcess via:

```julia μ = 1.0 σ = 2.0 W = GeometricBrownianMotionProcess(μ, σ, 0.0, 1.0, 1.0)

...

Define f,g,u0,tspan for a SDEProblem

...

prob = SDEProblem(f, g, u0, tspan, noise = W) ```

StochasticDiffEq.jl also handles solving random ordinary differential equations. This is shown in the RODE tutorial.

julia using StochasticDiffEq function f(u, p, t, W) 2u*sin(W) end u0 = 1.00 tspan = (0.0, 5.0) prob = RODEProblem(f, u0, tspan) sol = solve(prob, RandomEM(), dt = 1/100)

Available Solvers

For the list of available solvers, please refer to the DifferentialEquations.jl SDE Solvers page and the RODE Solvers page.

Owner

Name: SciML Open Source Scientific Machine Learning
Login: SciML
Kind: organization
Email: contact@chrisrackauckas.com

Website: https://sciml.ai
Twitter: SciML_Org
Repositories: 170
Profile: https://github.com/SciML

Open source software for scientific machine learning

Citation (CITATION.bib)

@article{DifferentialEquations.jl-2017,
 author = {Rackauckas, Christopher and Nie, Qing},
 doi = {10.5334/jors.151},
 journal = {The Journal of Open Research Software},
 keywords = {Applied Mathematics},
 note = {Exported from https://app.dimensions.ai on 2019/05/05},
 number = {1},
 pages = {},
 title = {DifferentialEquations.jl – A Performant and Feature-Rich Ecosystem for Solving Differential Equations in Julia},
 url = {https://app.dimensions.ai/details/publication/pub.1085583166 and http://openresearchsoftware.metajnl.com/articles/10.5334/jors.151/galley/245/download/},
 volume = {5},
 year = {2017}
}

@article{rackauckas2017adaptive,
  title={Adaptive methods for stochastic differential equations via natural embeddings and rejection sampling with memory},
  author={Rackauckas, Christopher and Nie, Qing},
  journal={Discrete and continuous dynamical systems. Series B},
  volume={22},
  number={7},
  pages={2731},
  year={2017},
  publisher={NIH Public Access}
}

GitHub Events

Total

Create event: 33
Issues event: 5
Release event: 14
Watch event: 42
Delete event: 21
Issue comment event: 74
Push event: 93
Pull request review comment event: 26
Pull request review event: 29
Pull request event: 68
Fork event: 9

Last Year

Create event: 33
Issues event: 5
Release event: 14
Watch event: 42
Delete event: 21
Issue comment event: 74
Push event: 93
Pull request review comment event: 26
Pull request review event: 29
Pull request event: 68
Fork event: 9

Committers

Last synced: 8 months ago

All Time

Total Commits: 1,988
Total Committers: 58
Avg Commits per committer: 34.276
Development Distribution Score (DDS): 0.446

Past Year

Commits: 117
Committers: 12
Avg Commits per committer: 9.75
Development Distribution Score (DDS): 0.462

Top Committers

Name	Email	Commits
Chris Rackauckas	a**s@c**m	1,102
deeepeshthakur	d**r@g**m	318
Frank Schaefer	f**r@u**h	184
Yingbo Ma	m**5@g**m	64
David Widmann	d**b@d**e	41
Ricardo Rosa	r**a@g**m	32
Kanav Gupta	k**0@g**m	31
Xingjian Guo	x**3@n**u	26
Aayush Sabharwal	a**l@g**m	16
CompatHelper Julia	c**y@j**g	15
Oscar Smith	o**h@g**m	14
github-actions[bot]	4****]	12
jClugstor	j**n@g**m	12
dependabot[bot]	4****]	8
Tatsuhiro Onodera	o**t@s**u	8
James Gardner	j**1@g**m	8
ErikQQY	2**3@q**m	8
Sam Isaacson	i****s	7
Takafumi Arakaki	a**f@g**m	6
Hossein Pourbozorg	p**g@g**m	6
Anas	a**r@g**m	5
Elliot Saba	s**t@g**m	5
Anant Thazhemadam	a**m@g**m	4
Vedant Puri	v**i@g**m	4
Hendrik Ranocha	m**l@r**e	4
Sikorski	s**i@z**e	4
Chris de Graaf	me@c****v	3
femtocleaner[bot]	f****]	3
Avik Pal	a**l@m**u	3
hlw	h**n@i**m	2
and 28 more...

Committer Domains (Top 20 + Academic)

qq.com: 2 chrisrackauckas.com: 1 unibas.ch: 1 devmotion.de: 1 nyu.edu: 1 julialang.org: 1 stanford.edu: 1 ranocha.de: 1 zib.de: 1 cdg.dev: 1 mit.edu: 1 juliacomputing.com: 1 universite-paris-saclay.fr: 1 kelman.net: 1 alum.mit.edu: 1 runbox.com: 1 chalmers.se: 1 leugering.science: 1 aluthge.com: 1 simons-rock.edu: 1 uchicago.edu: 1 physik.uni-muenchen.de: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 42
Total pull requests: 186
Average time to close issues: 4 months
Average time to close pull requests: about 1 month
Total issue authors: 24
Total pull request authors: 34
Average comments per issue: 10.43
Average comments per pull request: 0.91
Merged pull requests: 139
Bot issues: 0
Bot pull requests: 37

Past Year

Issues: 3
Pull requests: 59
Average time to close issues: about 23 hours
Average time to close pull requests: 4 days
Issue authors: 3
Pull request authors: 9
Average comments per issue: 1.67
Average comments per pull request: 0.29
Merged pull requests: 45
Bot issues: 0
Bot pull requests: 13

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Top Authors

Issue Authors

ChrisRackauckas (11)
oameye (3)
axsk (3)
jebej (3)
rmsrosa (2)
TorkelE (2)
AayushSabharwal (1)
JuliaTagBot (1)
jack-dunham (1)
Jonas-a-Zimmermann (1)
stochasticguy (1)
apkille (1)
tbilitewski (1)
Lightup1 (1)
frankschae (1)

Pull Request Authors

ChrisRackauckas (65)
github-actions[bot] (27)
AayushSabharwal (13)
dependabot[bot] (10)
oscardssmith (9)
frankschae (6)
rmsrosa (6)
prbzrg (4)
oameye (4)
jClugstor (4)
isaacsas (3)
ranocha (3)
vpuri3 (3)
BenChung (2)
apkille (2)

Top Labels

Issue Labels

bug (8) new-algorithm (4) question (2)

Pull Request Labels

dependencies (10) github_actions (1)

Packages

Total packages: 1
Total downloads:
- julia 4,102 total

Total dependent packages: 16
Total dependent repositories: 9
Total versions: 153

juliahub.com: StochasticDiffEq

Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem

Documentation: https://docs.juliahub.com/General/StochasticDiffEq/stable/
License: MIT
Latest release: 6.81.0
published 8 months ago

Versions: 153
Dependent Packages: 16
Dependent Repositories: 9
Downloads: 4,102 Total

Rankings

Forks count: 2.3%

Dependent repos count: 3.3%

Average: 3.4%

Stargazers count: 3.6%

Dependent packages count: 4.2%

Last synced: 6 months ago

Dependencies

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