SciMLExpectations

Fast uncertainty quantification for scientific machine learning (SciML) and differential equations

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

Science Score: 54.0%

This score indicates how likely this project is to be science-related based on various indicators:

✓
CITATION.cff file
Found CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
✓
.zenodo.json file
Found .zenodo.json file
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DOI references
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Academic publication links
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Committers with academic emails
2 of 25 committers (8.0%) from academic institutions
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Institutional organization owner
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JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (12.1%) to scientific vocabulary

Keywords

differential-equations differentialequations integration julia ode scientific-machine-learning sciml uncertainty-quantification uq

Keywords from Contributors

matrix-exponential pde sde numerical stochastic-differential-equations ordinary-differential-equations dde dae neural-ode partial-differential-equations

Last synced: 6 months ago · JSON representation ·

Repository

Fast uncertainty quantification for scientific machine learning (SciML) and differential equations

Basic Info

Host: GitHub
Owner: SciML
License: other
Language: Julia
Default Branch: master
Homepage: https://docs.sciml.ai/SciMLExpectations/stable/
Size: 70.1 MB

Statistics

Stars: 69
Watchers: 7
Forks: 20
Open Issues: 34
Releases: 18

Topics

differential-equations differentialequations integration julia ode scientific-machine-learning sciml uncertainty-quantification uq

Created over 9 years ago · Last pushed 6 months ago

Metadata Files

Readme License Citation

SciMLExpectations.jl: Expectated Values of Simulations and Uncertainty Quantification

This package is still under heavy construction. Use at your own risk!

SciMLExpectations.jl is a package for quantifying the uncertainties of simulations by calculating the expectations of observables with respect to input uncertainties. Its goal is to make it fast and easy to compute solution moments in a differentiable way in order to enable fast optimization under uncertainty.

Tutorials and Documentation

For information on using the package, see the stable documentation. Use the in-development documentation for the version of the documentation, which contains the unreleased features.

Example

```julia using SciMLExpectations, OrdinaryDiffEq, Distributions, Cubature

function eom!(du, u, p, t, A) du .= A * u end

u0 = [1.0, 1.0] tspan = (0.0, 3.0) p = [1.0; 2.0] A = [0.0 1.0; -p[1] -p[2]] prob = ODEProblem((du, u, p, t) -> eom!(du, u, p, t, A), u0, tspan, p) u0sdist = (Uniform(1, 10), truncated(Normal(3.0, 1), 0.0, 6.0)) gd = GenericDistribution(u0sdist...) cov(x, u, p) = x, p

sm = SystemMap(prob, Tsit5(), save_everystep = false)

analytical = (exp(A * tspan[end]) * [mean(d) for d in u0s_dist]) analytical ```

julia> analytical 2-element Vector{Float64}: 1.5433991194037804 -1.120209038276938

julia g(sol, p) = sol[:, end] exprob = ExpectationProblem(sm, g, cov, gd) sol = solve(exprob, Koopman(); quadalg = CubatureJLh(), ireltol = 1e-3, iabstol = 1e-3) sol.u # Expectation of the states 1 and 2 at the final time point

2-element Vector{Float64}: 1.5433860531082695 -1.1201922503747408

Approximate error on the expectation

sol.resid

=

2-element Vector{Float64}: 7.193424502016654e-5 5.2074632876847327e-5 =#

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}
}

GitHub Events

Total

Watch event: 4
Delete event: 10
Issue comment event: 14
Push event: 8
Pull request event: 31
Fork event: 1
Create event: 11

Last Year

Watch event: 4
Delete event: 10
Issue comment event: 14
Push event: 8
Pull request event: 31
Fork event: 1
Create event: 11

Committers

Last synced: 8 months ago

All Time

Total Commits: 281
Total Committers: 25
Avg Commits per committer: 11.24
Development Distribution Score (DDS): 0.658

Past Year

Commits: 15
Committers: 5
Avg Commits per committer: 3.0
Development Distribution Score (DDS): 0.667

Top Committers

Name	Email	Commits
Chris Rackauckas	a**s@c**m	96
Adam R. Gerlach	a**1@a**m	43
CompatHelper Julia	c**y@j**g	36
ArnoStrouwen	a**n@t**e	30
Adam R Gerlach	a**1@u**l	21
lxvm	l**o@v**m	12
dependabot[bot]	4****]	8
github-actions[bot]	4****]	5
Anant Thazhemadam	a**m@g**m	4
agerlach	5****h	4
txn2022	1****2	4
Andrew Leonard	a**1@g**m	3
Chris de Graaf	me@c****v	2
Krishna Bhogaonker	c**q@g**m	2
Anshul Singhvi	a**7@s**u	1
David Widmann	d****n	1
Hendrik Ranocha	m**l@r**e	1
Julia TagBot	5****t	1
Marcelo Forets	m**s@g**m	1
Pengfei Song	6****2	1
ScottPJones	s**s@a**u	1
Sharan Yalburgi	s**i@g**m	1
Vaibhav Kumar Dixit	v**t@g**m	1
owiecc	s**i@m**m	1
femtocleaner[bot]	f****]	1

Committer Domains (Top 20 + Academic)

me.com: 1 alum.mit.edu: 1 ranocha.de: 1 simons-rock.edu: 1 cdg.dev: 1 vanmunoz.com: 1 us.af.mil: 1 telenet.be: 1 julialang.org: 1 afresearchlab.com: 1 chrisrackauckas.com: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 42
Total pull requests: 138
Average time to close issues: 14 days
Average time to close pull requests: about 2 months
Total issue authors: 18
Total pull request authors: 17
Average comments per issue: 3.55
Average comments per pull request: 0.52
Merged pull requests: 95
Bot issues: 0
Bot pull requests: 78

Past Year

Issues: 0
Pull requests: 25
Average time to close issues: N/A
Average time to close pull requests: 6 days
Issue authors: 0
Pull request authors: 3
Average comments per issue: 0
Average comments per pull request: 0.04
Merged pull requests: 7
Bot issues: 0
Bot pull requests: 20

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Top Authors

Issue Authors

agerlach (13)
ArnoStrouwen (6)
lakshaya17 (4)
lindnemi (2)
sdwfrost (2)
owiecc (2)
ChrisRackauckas (2)
rfourquet (1)
matteoettam09 (1)
homocomputeris (1)
txn2022 (1)
evrenmturan (1)
djinnome (1)
S-Math (1)
aml5600 (1)

Pull Request Authors

github-actions[bot] (79)
ArnoStrouwen (23)
ChrisRackauckas (15)
dependabot[bot] (13)
agerlach (9)
thazhemadam (4)
christopher-dG (2)
sharanry (2)
00krishna (2)
aml5600 (1)
txn2022 (1)
ranocha (1)
devmotion (1)
Song921012 (1)
mforets (1)

Top Labels

Issue Labels

question (2)

Pull Request Labels

dependencies (13)

Packages

Total packages: 1
Total downloads:
- julia 1 total

Total dependent packages: 1
Total dependent repositories: 0
Total versions: 5

juliahub.com: SciMLExpectations

Fast uncertainty quantification for scientific machine learning (SciML) and differential equations

Homepage: https://docs.sciml.ai/SciMLExpectations/stable/
Documentation: https://docs.juliahub.com/General/SciMLExpectations/stable/
License: MIT
Latest release: 2.3.0
published 8 months ago

Versions: 5
Dependent Packages: 1
Dependent Repositories: 0
Downloads: 1 Total

Rankings

Forks count: 6.8%

Dependent repos count: 9.9%

Stargazers count: 11.1%

Average: 12.7%

Dependent packages count: 23.0%

Last synced: 6 months ago

Dependencies

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.github/workflows/CompatHelper.yml actions

julia-actions/setup-julia latest composite

.github/workflows/Documentation.yml actions

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.github/workflows/Invalidations.yml actions

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SciMLExpectations

Science Score: 54.0%

Keywords

Keywords from Contributors

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

SciMLExpectations.jl: Expectated Values of Simulations and Uncertainty Quantification

This package is still under heavy construction. Use at your own risk!

Tutorials and Documentation

Example

Approximate error on the expectation

=

Owner

Citation (CITATION.bib)

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

juliahub.com: SciMLExpectations

Rankings

Dependencies