ImplicitDiscreteSolve
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
Science Score: 54.0%
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✓CITATION.cff file
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Low similarity (13.3%) to scientific vocabulary
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Repository
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
Basic Info
- Host: GitHub
- Owner: SciML
- License: other
- Language: Julia
- Default Branch: master
- Homepage: https://diffeq.sciml.ai/latest/
- Size: 61.9 MB
Statistics
- Stars: 604
- Watchers: 17
- Forks: 233
- Open Issues: 381
- Releases: 490
Topics
Metadata Files
README.md
OrdinaryDiffEq.jl
OrdinaryDiffEq.jl is a component package in the DifferentialEquations ecosystem. It holds the ordinary differential equation solvers and utilities. While completely independent and usable on its own, users interested in using this functionality should check out DifferentialEquations.jl.
Installation
Assuming that you already have Julia correctly installed, it suffices to import OrdinaryDiffEq.jl in the standard way:
julia
import Pkg;
Pkg.add("OrdinaryDiffEq");
API
OrdinaryDiffEq.jl is part of the SciML common interface, but can be used independently of DifferentialEquations.jl. The only requirement is that the user passes an OrdinaryDiffEq.jl algorithm to solve. For example, we can solve the ODE tutorial from the docs using the Tsit5() algorithm:
julia
using OrdinaryDiffEq
f(u, p, t) = 1.01 * u
u0 = 1 / 2
tspan = (0.0, 1.0)
prob = ODEProblem(f, u0, tspan)
sol = solve(prob, Tsit5(), reltol = 1e-8, abstol = 1e-8)
using Plots
plot(sol, linewidth = 5, title = "Solution to the linear ODE with a thick line",
xaxis = "Time (t)", yaxis = "u(t) (in μm)", label = "My Thick Line!") # legend=false
plot!(sol.t, t -> 0.5 * exp(1.01t), lw = 3, ls = :dash, label = "True Solution!")
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
using OrdinaryDiffEq
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
u0 = [1.0; 0.0; 0.0]
tspan = (0.0, 100.0)
prob = ODEProblem(lorenz!, u0, tspan)
sol = solve(prob, Tsit5())
using Plots;
plot(sol, idxs = (1, 2, 3))
Very fast static array versions can be specifically compiled to the size of your model. For example:
julia
using OrdinaryDiffEq, StaticArrays
function lorenz(u, p, t)
SA[10.0(u[2] - u[1]), u[1] * (28.0 - u[3]) - u[2], u[1] * u[2] - (8 / 3) * u[3]]
end
u0 = SA[1.0; 0.0; 0.0]
tspan = (0.0, 100.0)
prob = ODEProblem(lorenz, u0, tspan)
sol = solve(prob, Tsit5())
For "refined ODEs", like dynamical equations and SecondOrderODEProblems, refer to the DiffEqDocs. For example, in DiffEqTutorials.jl we show how to solve equations of motion using symplectic methods:
julia
function HH_acceleration!(dv, v, u, p, t)
x, y = u
dx, dy = dv
dv[1] = -x - 2x * y
dv[2] = y^2 - y - x^2
end
initial_positions = [0.0, 0.1]
initial_velocities = [0.5, 0.0]
prob = SecondOrderODEProblem(HH_acceleration!, initial_velocities, initial_positions, tspan)
sol2 = solve(prob, KahanLi8(), dt = 1 / 10);
Other refined forms are IMEX and semi-linear ODEs (for exponential integrators).
Available Solvers
For the list of available solvers, please refer to the DifferentialEquations.jl ODE Solvers, Dynamical ODE Solvers, and the Split ODE Solvers pages.
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}
}
Committers
Last synced: 8 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Christopher Rackauckas | a****s@c****m | 3,220 |
| Yingbo Ma | m****5@g****m | 785 |
| ParamThakkar123 | p****4@g****m | 571 |
| jClugstor | j****n@g****m | 367 |
| oscarddssmith | o****h@j****m | 305 |
| David Widmann | d****b@d****e | 238 |
| Xingjian Guo | x****3@n****u | 215 |
| Utkarsh | r****0@g****m | 213 |
| Hendrik Ranocha | m****l@r****e | 209 |
| Kanav Gupta | k****0@g****m | 173 |
| deeepeshthakur | d****r@g****m | 145 |
| Shreyas Ekanathan | s****9@g****m | 139 |
| sipah00 | s****0@g****m | 116 |
| ArnoStrouwen | a****n@t****e | 104 |
| Saurabh Agarwal | s****l@g****m | 97 |
| CompatHelper Julia | c****y@j****g | 80 |
| Vedant Puri | v****i@g****m | 68 |
| Biswajit Ghosh | b****8@i****n | 58 |
| ErikQQY | 2****3@q****m | 53 |
| Collin Wittenstein | c****s@s****e | 51 |
| Chris Elrod | e****c@g****m | 48 |
| Junpeng | j****x@g****m | 48 |
| Diogo Netto | d****n@g****m | 44 |
| hlw | h****n@i****m | 40 |
| Henry Langner | 1****r | 38 |
| Gerd Steinebach | G****h@h****e | 36 |
| Gaurav Arya | g****2@g****m | 32 |
| Konstantin Althaus | k****s@t****e | 29 |
| Pepijn de Vos | p****s@j****m | 25 |
| Aayush Sabharwal | a****l@g****m | 25 |
| and 126 more... | ||
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 265
- Total pull requests: 1,177
- Average time to close issues: 6 months
- Average time to close pull requests: about 2 months
- Total issue authors: 128
- Total pull request authors: 85
- Average comments per issue: 9.97
- Average comments per pull request: 1.95
- Merged pull requests: 808
- Bot issues: 0
- Bot pull requests: 197
Past Year
- Issues: 92
- Pull requests: 476
- Average time to close issues: 7 days
- Average time to close pull requests: 5 days
- Issue authors: 57
- Pull request authors: 42
- Average comments per issue: 2.33
- Average comments per pull request: 1.63
- Merged pull requests: 329
- Bot issues: 0
- Bot pull requests: 35
Top Authors
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- ranocha (18)
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- Ickaser (4)
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- andreasnoack (4)
- jClugstor (3)
- homocomputeris (3)
Pull Request Authors
- ChrisRackauckas (304)
- oscardssmith (193)
- github-actions[bot] (186)
- ParamThakkar123 (52)
- jClugstor (47)
- AayushSabharwal (34)
- ChrisRackauckas-Claude (25)
- Shreyas-Ekanathan (22)
- ranocha (19)
- termi-official (16)
- ArnoStrouwen (16)
- ErikQQY (15)
- vyudu (14)
- gstein3m (13)
- cwittens (13)
Top Labels
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Packages
- Total packages: 36
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Total downloads:
- julia 166,249 total
-
Total dependent packages: 137
(may contain duplicates) -
Total dependent repositories: 59
(may contain duplicates) - Total versions: 699
juliahub.com: OrdinaryDiffEq
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Homepage: https://diffeq.sciml.ai/latest/
- Documentation: https://docs.juliahub.com/General/OrdinaryDiffEq/stable/
- License: MIT
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Latest release: 6.102.0
published 7 months ago
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juliahub.com: OrdinaryDiffEqCore
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Homepage: https://diffeq.sciml.ai/latest/
- Documentation: https://docs.juliahub.com/General/OrdinaryDiffEqCore/stable/
- License: MIT
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Latest release: 1.30.0
published 7 months ago
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juliahub.com: OrdinaryDiffEqDifferentiation
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Homepage: https://diffeq.sciml.ai/latest/
- Documentation: https://docs.juliahub.com/General/OrdinaryDiffEqDifferentiation/stable/
- License: MIT
-
Latest release: 1.14.0
published 7 months ago
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juliahub.com: OrdinaryDiffEqRosenbrock
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Homepage: https://diffeq.sciml.ai/latest/
- Documentation: https://docs.juliahub.com/General/OrdinaryDiffEqRosenbrock/stable/
- License: MIT
-
Latest release: 1.17.0
published 7 months ago
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juliahub.com: OrdinaryDiffEqBDF
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Homepage: https://diffeq.sciml.ai/latest/
- Documentation: https://docs.juliahub.com/General/OrdinaryDiffEqBDF/stable/
- License: MIT
-
Latest release: 1.10.1
published 7 months ago
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juliahub.com: OrdinaryDiffEqSDIRK
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Homepage: https://diffeq.sciml.ai/latest/
- Documentation: https://docs.juliahub.com/General/OrdinaryDiffEqSDIRK/stable/
- License: MIT
-
Latest release: 1.7.0
published 7 months ago
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juliahub.com: OrdinaryDiffEqFIRK
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Homepage: https://diffeq.sciml.ai/latest/
- Documentation: https://docs.juliahub.com/General/OrdinaryDiffEqFIRK/stable/
- License: MIT
-
Latest release: 1.16.0
published 7 months ago
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juliahub.com: OrdinaryDiffEqExtrapolation
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Homepage: https://diffeq.sciml.ai/latest/
- Documentation: https://docs.juliahub.com/General/OrdinaryDiffEqExtrapolation/stable/
- License: MIT
-
Latest release: 1.8.0
published 7 months ago
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juliahub.com: OrdinaryDiffEqExponentialRK
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Homepage: https://diffeq.sciml.ai/latest/
- Documentation: https://docs.juliahub.com/General/OrdinaryDiffEqExponentialRK/stable/
- License: MIT
-
Latest release: 1.8.0
published 7 months ago
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juliahub.com: OrdinaryDiffEqIMEXMultistep
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Homepage: https://diffeq.sciml.ai/latest/
- Documentation: https://docs.juliahub.com/General/OrdinaryDiffEqIMEXMultistep/stable/
- License: MIT
-
Latest release: 1.7.0
published 7 months ago
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juliahub.com: OrdinaryDiffEqStabilizedIRK
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Homepage: https://diffeq.sciml.ai/latest/
- Documentation: https://docs.juliahub.com/General/OrdinaryDiffEqStabilizedIRK/stable/
- License: MIT
-
Latest release: 1.6.0
published 7 months ago
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juliahub.com: OrdinaryDiffEqPDIRK
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Homepage: https://diffeq.sciml.ai/latest/
- Documentation: https://docs.juliahub.com/General/OrdinaryDiffEqPDIRK/stable/
- License: MIT
-
Latest release: 1.6.0
published 7 months ago
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juliahub.com: OrdinaryDiffEqDefault
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Homepage: https://diffeq.sciml.ai/latest/
- Documentation: https://docs.juliahub.com/General/OrdinaryDiffEqDefault/stable/
- License: MIT
-
Latest release: 1.8.0
published 7 months ago
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juliahub.com: OrdinaryDiffEqNonlinearSolve
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Homepage: https://diffeq.sciml.ai/latest/
- Documentation: https://docs.juliahub.com/General/OrdinaryDiffEqNonlinearSolve/stable/
- License: MIT
-
Latest release: 1.14.1
published 7 months ago
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juliahub.com: OrdinaryDiffEqTsit5
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Homepage: https://diffeq.sciml.ai/latest/
- Documentation: https://docs.juliahub.com/General/OrdinaryDiffEqTsit5/stable/
- License: MIT
-
Latest release: 1.5.0
published 7 months ago
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juliahub.com: OrdinaryDiffEqLowOrderRK
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Homepage: https://diffeq.sciml.ai/latest/
- Documentation: https://docs.juliahub.com/General/OrdinaryDiffEqLowOrderRK/stable/
- License: MIT
-
Latest release: 1.6.0
published 7 months ago
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juliahub.com: OrdinaryDiffEqVerner
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Homepage: https://diffeq.sciml.ai/latest/
- Documentation: https://docs.juliahub.com/General/OrdinaryDiffEqVerner/stable/
- License: MIT
-
Latest release: 1.6.0
published 7 months ago
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juliahub.com: OrdinaryDiffEqFunctionMap
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Homepage: https://diffeq.sciml.ai/latest/
- Documentation: https://docs.juliahub.com/General/OrdinaryDiffEqFunctionMap/stable/
- License: MIT
-
Latest release: 1.5.0
published 7 months ago
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juliahub.com: OrdinaryDiffEqLowStorageRK
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Homepage: https://diffeq.sciml.ai/latest/
- Documentation: https://docs.juliahub.com/General/OrdinaryDiffEqLowStorageRK/stable/
- License: MIT
-
Latest release: 1.6.0
published 7 months ago
Rankings
juliahub.com: OrdinaryDiffEqStabilizedRK
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Homepage: https://diffeq.sciml.ai/latest/
- Documentation: https://docs.juliahub.com/General/OrdinaryDiffEqStabilizedRK/stable/
- License: MIT
-
Latest release: 1.4.0
published 7 months ago
Rankings
juliahub.com: OrdinaryDiffEqSSPRK
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Homepage: https://diffeq.sciml.ai/latest/
- Documentation: https://docs.juliahub.com/General/OrdinaryDiffEqSSPRK/stable/
- License: MIT
-
Latest release: 1.6.0
published 7 months ago
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juliahub.com: OrdinaryDiffEqExplicitRK
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Homepage: https://diffeq.sciml.ai/latest/
- Documentation: https://docs.juliahub.com/General/OrdinaryDiffEqExplicitRK/stable/
- License: MIT
-
Latest release: 1.4.0
published 7 months ago
Rankings
juliahub.com: OrdinaryDiffEqRKN
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Homepage: https://diffeq.sciml.ai/latest/
- Documentation: https://docs.juliahub.com/General/OrdinaryDiffEqRKN/stable/
- License: MIT
-
Latest release: 1.5.0
published 7 months ago
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juliahub.com: OrdinaryDiffEqNordsieck
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Homepage: https://diffeq.sciml.ai/latest/
- Documentation: https://docs.juliahub.com/General/OrdinaryDiffEqNordsieck/stable/
- License: MIT
-
Latest release: 1.4.0
published 7 months ago
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juliahub.com: OrdinaryDiffEqAdamsBashforthMoulton
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Homepage: https://diffeq.sciml.ai/latest/
- Documentation: https://docs.juliahub.com/General/OrdinaryDiffEqAdamsBashforthMoulton/stable/
- License: MIT
-
Latest release: 1.5.0
published 7 months ago
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juliahub.com: OrdinaryDiffEqQPRK
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Homepage: https://diffeq.sciml.ai/latest/
- Documentation: https://docs.juliahub.com/General/OrdinaryDiffEqQPRK/stable/
- License: MIT
-
Latest release: 1.4.0
published 7 months ago
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juliahub.com: OrdinaryDiffEqHighOrderRK
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Homepage: https://diffeq.sciml.ai/latest/
- Documentation: https://docs.juliahub.com/General/OrdinaryDiffEqHighOrderRK/stable/
- License: MIT
-
Latest release: 1.5.0
published 7 months ago
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juliahub.com: OrdinaryDiffEqLinear
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Homepage: https://diffeq.sciml.ai/latest/
- Documentation: https://docs.juliahub.com/General/OrdinaryDiffEqLinear/stable/
- License: MIT
-
Latest release: 1.6.0
published 7 months ago
Rankings
juliahub.com: OrdinaryDiffEqSymplecticRK
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Homepage: https://diffeq.sciml.ai/latest/
- Documentation: https://docs.juliahub.com/General/OrdinaryDiffEqSymplecticRK/stable/
- License: MIT
-
Latest release: 1.7.0
published 7 months ago
Rankings
juliahub.com: OrdinaryDiffEqFeagin
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Homepage: https://diffeq.sciml.ai/latest/
- Documentation: https://docs.juliahub.com/General/OrdinaryDiffEqFeagin/stable/
- License: MIT
-
Latest release: 1.4.0
published 7 months ago
Rankings
juliahub.com: OrdinaryDiffEqPRK
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Homepage: https://diffeq.sciml.ai/latest/
- Documentation: https://docs.juliahub.com/General/OrdinaryDiffEqPRK/stable/
- License: MIT
-
Latest release: 1.4.0
published 7 months ago
Rankings
juliahub.com: OrdinaryDiffEqFIRKGenerator
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Homepage: https://diffeq.sciml.ai/latest/
- Documentation: https://docs.juliahub.com/General/OrdinaryDiffEqFIRKGenerator/stable/
- License: MIT
-
Latest release: 1.1.0
published over 1 year ago
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juliahub.com: OrdinaryDiffEqSIMDRK
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Homepage: https://diffeq.sciml.ai/latest/
- Documentation: https://docs.juliahub.com/General/OrdinaryDiffEqSIMDRK/stable/
- License: MIT
-
Latest release: 1.1.0
published 7 months ago
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juliahub.com: OrdinaryDiffEqTaylorSeries
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Homepage: https://diffeq.sciml.ai/latest/
- Documentation: https://docs.juliahub.com/General/OrdinaryDiffEqTaylorSeries/stable/
- License: MIT
-
Latest release: 1.4.0
published 7 months ago
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juliahub.com: SimpleImplicitDiscreteSolve
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Homepage: https://diffeq.sciml.ai/latest/
- Documentation: https://docs.juliahub.com/General/SimpleImplicitDiscreteSolve/stable/
- License: MIT
-
Latest release: 1.2.0
published 7 months ago
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juliahub.com: ImplicitDiscreteSolve
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Homepage: https://diffeq.sciml.ai/latest/
- Documentation: https://docs.juliahub.com/General/ImplicitDiscreteSolve/stable/
- License: MIT
-
Latest release: 1.2.0
published 7 months ago
Rankings
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