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MultiScaleArrays

A framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations

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

Science Score: 54.0%

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    Low similarity (14.8%) to scientific vocabulary

Keywords

automatic-differentiation dae dde differential-equations differentialequations hybrid-differential-equations julia models multiscale neural-differential-equations neural-ode ode scientific-ai scientific-machine-learning scientific-ml sciml sde

Keywords from Contributors

matrix-exponential pdes numerical stochastic-differential-equations differential-algebraic-equations ordinary-differential-equations stochastic-processes partial-differential-equations estimation integration
Last synced: 6 months ago · JSON representation ·

Repository

A framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations

Basic Info
Statistics
  • Stars: 76
  • Watchers: 4
  • Forks: 17
  • Open Issues: 11
  • Releases: 29
Topics
automatic-differentiation dae dde differential-equations differentialequations hybrid-differential-equations julia models multiscale neural-differential-equations neural-ode ode scientific-ai scientific-machine-learning scientific-ml sciml sde
Created over 9 years ago · Last pushed 6 months ago
Metadata Files
Readme License Citation

README.md

MultiScaleArrays

Join the chat at https://julialang.zulipchat.com #sciml-bridged Global Docs

codecov Build Status

ColPrac: Contributor's Guide on Collaborative Practices for Community Packages SciML Code Style

MultiScaleArrays.jl allows you to easily build multiple scale models which are fully compatible with native Julia scientific computing packages like DifferentialEquations.jl or Optim.jl. These models utilize a tree structure to describe phenomena of multiple scales, but the interface allows you to describe equations on different levels, using aggregations from lower levels to describe complex systems. Their structure allows for complex and dynamic models to be developed with only a small performance difference. In the end, they present themselves as an AbstractArray to standard solvers, allowing them to be used in place of a Vector in any appropriately made Julia package.

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

The usage is best described by an example. Here we build a hierarchy where Embryos contain Tissues which contain Populations which contain Cells, and the cells contain proteins whose concentrations are modeled as simply a vector of numbers (it can be anything linearly indexable).

julia using MultiScaleArrays struct Cell{B} <: AbstractMultiScaleArrayLeaf{B} values::Vector{B} end struct Population{T <: AbstractMultiScaleArray, B <: Number} <: AbstractMultiScaleArray{B} nodes::Vector{T} values::Vector{B} end_idxs::Vector{Int} end struct Tissue{T <: AbstractMultiScaleArray, B <: Number} <: AbstractMultiScaleArray{B} nodes::Vector{T} values::Vector{B} end_idxs::Vector{Int} end struct Embryo{T <: AbstractMultiScaleArray, B <: Number} <: AbstractMultiScaleArrayHead{B} nodes::Vector{T} values::Vector{B} end_idxs::Vector{Int} end

This setup defines a type structure which is both a tree and an array. A picture of a possible version is the following:

Let's build a version of this. Using the constructors we can directly construct leaf types: julia cell1 = Cell([1.0; 2.0; 3.0]) cell2 = Cell([4.0; 5.0])

and build types higher up in the hierarchy by using the construct method. The method is construct(T::AbstractMultiScaleArray, nodes, values), though if values is not given it's taken to be empty.

julia cell3 = Cell([3.0; 2.0; 5.0]) cell4 = Cell([4.0; 6.0]) population = construct(Population, deepcopy([cell1, cell3, cell4])) population2 = construct(Population, deepcopy([cell1, cell3, cell4])) population3 = construct(Population, deepcopy([cell1, cell3, cell4])) tissue1 = construct(Tissue, deepcopy([population, population2, population3])) # Make a Tissue from Populations tissue2 = construct(Tissue, deepcopy([population2, population, population3])) embryo = construct(Embryo, deepcopy([tissue1, tissue2])) # Make an embryo from Tissues

Owner

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

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
  • Create event: 6
  • Release event: 1
  • Issues event: 3
  • Watch event: 1
  • Delete event: 6
  • Issue comment event: 14
  • Push event: 24
  • Pull request event: 16
  • Fork event: 2
Last Year
  • Create event: 6
  • Release event: 1
  • Issues event: 3
  • Watch event: 1
  • Delete event: 6
  • Issue comment event: 14
  • Push event: 24
  • Pull request event: 16
  • Fork event: 2

Committers

Last synced: 8 months ago

All Time
  • Total Commits: 247
  • Total Committers: 22
  • Avg Commits per committer: 11.227
  • Development Distribution Score (DDS): 0.36
Past Year
  • Commits: 6
  • Committers: 4
  • Avg Commits per committer: 1.5
  • Development Distribution Score (DDS): 0.667
Top Committers
Name Email Commits
ChrisRackauckas me@c****m 158
Mathé-Hubert 3****H 27
Arno Strouwen a****n@t****e 10
ScottPJones s****s@a****u 9
Rafael Schouten r****n@g****m 7
dependabot[bot] 4****] 7
Anant Thazhemadam a****m@g****m 6
github-actions[bot] 4****] 3
Oscar Smith o****h@j****m 3
CompatHelper Julia c****y@j****g 2
femtocleaner[bot] f****] 2
Chris de Graaf me@c****v 2
Aayush Sabharwal a****l@j****m 2
Anshul Singhvi a****7@s****u 1
David Widmann d****n 1
Elliot Saba s****t@g****m 1
Hendrik Ranocha m****l@r****e 1
Julia TagBot 5****t 1
Nikos Pitsianis p****s@y****m 1
Sathvik Bhagavan s****n@g****m 1
Pepijn de Vos p****s@j****m 1
Tony Kelman t****y@k****t 1
Committer Domains (Top 20 + Academic)
juliacomputing.com: 2 kelman.net: 1 ranocha.de: 1 simons-rock.edu: 1 juliahub.com: 1 cdg.dev: 1 julialang.org: 1 alum.mit.edu: 1 telenet.be: 1 chrisrackauckas.com: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 31
  • Total pull requests: 75
  • Average time to close issues: 2 months
  • Average time to close pull requests: 3 days
  • Total issue authors: 13
  • Total pull request authors: 22
  • Average comments per issue: 3.35
  • Average comments per pull request: 0.59
  • Merged pull requests: 55
  • Bot issues: 0
  • Bot pull requests: 16
Past Year
  • Issues: 3
  • Pull requests: 12
  • Average time to close issues: 2 days
  • Average time to close pull requests: about 10 hours
  • Issue authors: 3
  • Pull request authors: 5
  • Average comments per issue: 0.67
  • Average comments per pull request: 0.25
  • Merged pull requests: 5
  • Bot issues: 0
  • Bot pull requests: 1
View more stats
Top Authors
Issue Authors
  • ChrisRackauckas (16)
  • zahachtah (3)
  • ArnoStrouwen (2)
  • KZiemian (1)
  • vlepori (1)
  • RobertGregg (1)
  • djsegal (1)
  • jClugstor (1)
  • JuliaTagBot (1)
  • HugoMH (1)
  • dlfivefifty (1)
  • mcfefa (1)
Pull Request Authors
  • ChrisRackauckas (16)
  • dependabot[bot] (11)
  • ArnoStrouwen (10)
  • ScottPJones (6)
  • github-actions[bot] (6)
  • HugoMH (6)
  • thazhemadam (4)
  • oscardssmith (4)
  • pitsianis (2)
  • femtocleaner[bot] (2)
  • christopher-dG (2)
  • AayushSabharwal (2)
  • JuliaTagBot (1)
  • pepijndevos (1)
  • rafaqz (1)
Top Labels
Issue Labels
question (1) bug (1)
Pull Request Labels
dependencies (11) github_actions (1)

Packages

  • Total packages: 1
  • Total downloads:
    • julia 39 total
  • Total dependent packages: 2
  • Total dependent repositories: 2
  • Total versions: 20
juliahub.com: MultiScaleArrays

A framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations

  • Versions: 20
  • Dependent Packages: 2
  • Dependent Repositories: 2
  • Downloads: 39 Total
Rankings
Dependent repos count: 7.3%
Stargazers count: 10.1%
Forks count: 10.7%
Average: 11.3%
Dependent packages count: 17.3%
Last synced: 6 months ago

Dependencies

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