delaydiffeq.jl-bcd4f6db-9728-5f36-b5f7-82caef46ccdb
Last snapshots taken from https://github.com/UnofficialJuliaMirror/DelayDiffEq.jl-bcd4f6db-9728-5f36-b5f7-82caef46ccdb on 2019-11-20T06:10:25.458-05:00 by @UnofficialJuliaMirrorBot via Travis job 153.11 , triggered by Travis cron job on branch "master"
Science Score: 18.0%
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✓CITATION.cff file
Found CITATION.cff file -
○codemeta.json file
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○.zenodo.json file
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Low similarity (10.3%) to scientific vocabulary
Repository
Last snapshots taken from https://github.com/UnofficialJuliaMirror/DelayDiffEq.jl-bcd4f6db-9728-5f36-b5f7-82caef46ccdb on 2019-11-20T06:10:25.458-05:00 by @UnofficialJuliaMirrorBot via Travis job 153.11 , triggered by Travis cron job on branch "master"
Basic Info
- Host: GitHub
- Owner: UnofficialJuliaMirrorSnapshots
- License: other
- Language: Julia
- Default Branch: master
- Size: 176 KB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
DelayDiffEq.jl
DelayDiffEq.jl is a component package in the DifferentialEquations ecosystem. It holds the delay differential equation solvers and utilities. It is built on top of OrdinaryDiffEq to extend those solvers for delay differential equations. While completely independent and usable on its own, users interested in using this functionality should check out DifferentialEquations.jl.
API
DelayDiffEq.jl is part of the JuliaDiffEq common interface, but can be used independently of DifferentialEquations.jl. The only requirement is that the user passes a DelayDiffEq.jl algorithm to solve. For example, we can solve the DDE tutorial from the documentation using the MethodOfSteps(Tsit5()) algorithm:
julia
using DelayDiffEq
const p0 = 0.2; const q0 = 0.3; const v0 = 1; const d0 = 5
const p1 = 0.2; const q1 = 0.3; const v1 = 1; const d1 = 1
const d2 = 1; const beta0 = 1; const beta1 = 1; const tau = 1
function bc_model(du,u,h,p,t)
du[1] = (v0/(1+beta0*(h(p, t-tau)[3]^2))) * (p0 - q0)*u[1] - d0*u[1]
du[2] = (v0/(1+beta0*(h(p, t-tau)[3]^2))) * (1 - p0 + q0)*u[1] +
(v1/(1+beta1*(h(p, t-tau)[3]^2))) * (p1 - q1)*u[2] - d1*u[2]
du[3] = (v1/(1+beta1*(h(p, t-tau)[3]^2))) * (1 - p1 + q1)*u[2] - d2*u[3]
end
lags = [tau]
h(p, t) = ones(3)
tspan = (0.0,10.0)
u0 = [1.0,1.0,1.0]
prob = DDEProblem(bc_model,u0,h,tspan,constant_lags = lags)
alg = MethodOfSteps(Tsit5())
sol = solve(prob,alg)
using Plots; plot(sol)
Both constant and state-dependent lags are supported. Interfacing with OrdinaryDiffEq.jl for implicit methods for stiff equations is also supported.
Available Solvers
For the list of available solvers, please refer to the DifferentialEquations.jl DDE Solvers page. For options for the solve command, see the common solver options page.
Owner
- Name: Unofficial Julia Mirror [Snapshots]
- Login: UnofficialJuliaMirrorSnapshots
- Kind: organization
- Website: https://github.com/UnofficialJuliaMirrorSnapshots/RepoSnapshots.jl
- Repositories: 4
- Profile: https://github.com/UnofficialJuliaMirrorSnapshots
Snapshots of all registered Julia packages. Updated weekly by @UnofficialJuliaMirrorBot. See also: @UnofficialJuliaMirror.
Citation (CITATION.bib)
@article{DifferentialEquations.jl-2017,
author = {Rackauckas, Christopher and Nie, Qing},
doi = {10.5334/jors.151},
journal = {The Journal of Open Source 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}
}