视频图matlab代码-DifferentialEquations.jl-0c46a032-eb83-5123-abaf-570

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  • 2022-05-06 02:34
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视频图matlab代码微分方程 这是一套用于对用Julia写的微分方程进行数值求解的套件,可在Julia,Python和R中使用。该软件包的目的是为各种微分方程提供有效的Julia求解器实现。 此软件包范围内的方程式包括: 离散方程(功能图,离散随机(Gillespie / Markov)模拟) 常微分方程(ODE) 拆分和分区的ODE(符号积分器,IMEX方法) 随机常微分方程(SODE或SDE) 随机微分方程(RODE或RDE) 微分代数方程(DAE) 延迟微分方程(DDE) 混合离散和连续方程(混合方程,跳跃扩散) (随机)偏微分方程((S)PDEs)(同时使用有限差分法和有限元方法) 经过良好优化的DifferentialEquations求解器使用一些经典算法以及最近的研究得出的基准,是一些最快的实现方案,这些算法通常优于“标准” C / Fortran方法,并且包括针对高精度和HPC应用进行了优化的算法。 同时,它包装了经典的C / Fortran方法,从而可以在需要时轻松切换到它们。 只需更改一行代码,即可使用不同语言和软件包的不同方法来求解微分方程,从而轻松进行基准测试
DifferentialEquations_jl-0c46a032-eb83-5123-abaf-570d42b7fbaa-master.zip
  • DifferentialEquations.jl-0c46a032-eb83-5123-abaf-570d42b7fbaa-master
  • src
  • DifferentialEquations.jl
    921B
  • sde_default_alg.jl
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  • dae_default_alg.jl
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  • default_arg_parsing.jl
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  • default_solve.jl
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  • rode_default_alg.jl
    433B
  • dde_default_alg.jl
    1.1KB
  • steady_state_default_alg.jl
    442B
  • discrete_default_alg.jl
    440B
  • ode_default_alg.jl
    2.2KB
  • assets
  • DifferentialEquations_Example.svg
    181.6KB
  • DifferentialEquations_Example.png
    272.7KB
  • test
  • default_dae_alg_test.jl
    274B
  • default_steady_state_alg_test.jl
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  • runtests.jl
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  • default_sde_alg_test.jl
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  • default_ode_alg_test.jl
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  • default_discrete_alg_test.jl
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  • default_rode_alg_test.jl
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  • default_dde_alg_test.jl
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  • CITATION.bib
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  • .gitattributes
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  • Project.toml
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  • .travis.yml
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  • LICENSE.md
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  • README.md
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  • appveyor.yml
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  • .gitignore
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内容介绍
# DifferentialEquations.jl [![Join the chat at https://gitter.im/JuliaDiffEq/Lobby](https://badges.gitter.im/JuliaDiffEq/Lobby.svg)](https://gitter.im/JuliaDiffEq/Lobby?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) [![Travis](https://travis-ci.org/JuliaDiffEq/DifferentialEquations.jl.svg?branch=master)](https://travis-ci.org/JuliaDiffEq/DifferentialEquations.jl) [![AppVoyer](https://ci.appveyor.com/api/projects/status/1smlr9ryfqfx1ear?svg=true)](https://ci.appveyor.com/project/ChrisRackauckas/differentialequations-jl-1sx90) [![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](http://docs.juliadiffeq.org/stable/) [![Latest](https://img.shields.io/badge/docs-latest-blue.svg)](http://docs.juliadiffeq.org/latest/) [![DOI](https://zenodo.org/badge/58516043.svg)](https://zenodo.org/badge/latestdoi/58516043) This is a suite for numerically solving differential equations written in Julia and available for use in Julia, Python, and R. The purpose of this package is to supply efficient Julia implementations of solvers for various differential equations. Equations within the realm of this package include: - Discrete equations (function maps, discrete stochastic (Gillespie/Markov) simulations) - Ordinary differential equations (ODEs) - Split and Partitioned ODEs (Symplectic integrators, IMEX Methods) - Stochastic ordinary differential equations (SODEs or SDEs) - Random differential equations (RODEs or RDEs) - Differential algebraic equations (DAEs) - Delay differential equations (DDEs) - Mixed discrete and continuous equations (Hybrid Equations, Jump Diffusions) - (Stochastic) partial differential equations ((S)PDEs) (with both finite difference and finite element methods) The well-optimized DifferentialEquations solvers benchmark as the some of the fastest implementations, using classic algorithms and ones from recent research which routinely outperform the "standard" C/Fortran methods, and include algorithms optimized for high-precision and HPC applications. At the same time, it wraps the classic C/Fortran methods, making it easy to switch over to them whenever necessary. Solving differential equations with different methods from different languages and packages can be done by changing one line of code, allowing for easy benchmarking to ensure you are using the fastest method possible. DifferentialEquations.jl integrates with the Julia package sphere with: - GPU accleration through CUDAnative.jl and CuArrays.jl - Automated sparsity detection with [SparsityDetection.jl](https://github.com/JuliaDiffEq/SparsityDetection.jl) - Automatic Jacobian coloring with [SparseDiffTools.jl](https://github.com/JuliaDiffEq/SparseDiffTools.jl), allowing for fast solutions to problems with sparse or structured (Tridiagonal, Banded, BlockBanded, etc.) Jacobians - Allowing the specification of linear solvers for maximal efficiency - Progress meter integration with the Juno IDE for estimated time to solution - Automatic plotting of time series and phase plots - Built-in interpolations - Wraps for common C/Fortran methods like Sundials and Hairer's radau - Arbitrary precision with BigFloats and Arbfloats - Arbitrary array types, allowing the definition of differential equations on matrices and distributed arrays - Unit checked arithmetic with Unitful Additionally, DifferentialEquations.jl comes with built-in analysis features, including: - [Forward and adjoint local sensitivity analysis](http://docs.juliadiffeq.org/latest/analysis/sensitivity.html) for fast gradient computations - [Optimization-based and Bayesian parameter estimation](http://docs.juliadiffeq.org/latest/analysis/parameter_estimation.html) - Neural differential equations with [DiffEqFlux.jl](https://github.com/JuliaDiffEq/DiffEqFlux.jl) for efficient scientific machine learning (scientific ML) and scientific AI. - [Automatic distributed, multithreaded, and GPU parallelism of ensemble trajectories](http://docs.juliadiffeq.org/latest/features/ensemble.html) - [Global sensitivity analysis](http://docs.juliadiffeq.org/latest/analysis/global_sensitivity.html) - [Uncertainty quantification](http://docs.juliadiffeq.org/latest/analysis/uncertainty_quantification.html) This gives a powerful mixture of speed and productivity features to help you solve and analyze your differential equations faster. For information on using the package, [see the stable documentation](http://docs.juliadiffeq.org/stable/). Use the [latest documentation](http://docs.juliadiffeq.org/latest/) for the version of the documentation which contains the un-released features. All of the algorithms are thoroughly tested to ensure accuracy via convergence tests. The algorithms are continuously tested to show correctness. IJulia tutorial notebooks [can be found at DiffEqTutorials.jl](https://github.com/JuliaDiffEq/DiffEqTutorials.jl). Benchmarks [can be found at DiffEqBenchmarks.jl](https://github.com/JuliaDiffEq/DiffEqBenchmarks.jl). If you find any equation where there seems to be an error, please open an issue. If you have any questions, or just want to chat about solvers/using the package, please feel free to chat in the [Gitter channel](https://gitter.im/JuliaDiffEq/Lobby?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge). For bug reports, feature requests, etc., please submit an issue. If you're interested in contributing, please see the [Developer Documentation](https://juliadiffeq.github.io/DiffEqDevDocs.jl/latest/). ## Supporting and Citing The software in this ecosystem was developed as part of academic research. If you would like to help support it, please star the repository as such metrics may help us secure funding in the future. If you use JuliaDiffEq software as part of your research, teaching, or other activities, we would be grateful if you could cite our work. [Please see our citation page for guidelines](http://juliadiffeq.org/citing.html). -------------------------------- ## Video Tutorial [![Video Tutorial](https://user-images.githubusercontent.com/1814174/36342812-bdfd0606-13b8-11e8-9eff-ff219de909e5.PNG)](https://youtu.be/KPEqYtEd-zY) ## Video Introduction [![Video Introduction to DifferentialEquations.jl](https://user-images.githubusercontent.com/1814174/27973992-e236a9a4-6310-11e7-84af-2b66097cecf9.PNG)](https://youtu.be/75SCMIRlNXM) ## Comparison to MATLAB, R, Julia, Python, C, Mathematica, Maple, and Fortran <a href="http://www.stochasticlifestyle.com/wp-content/uploads/2019/08/de_solver_software_comparsion.pdf" rel='nofollow' onclick='return false;'><img src="http://www.stochasticlifestyle.com/wp-content/uploads/2019/08/de_solver_software_comparsion-1.png" alt="Comparison Of Differential Equation Solver Software" align="middle"/></a> [See the corresponding blog post](http://www.stochasticlifestyle.com/comparison-differential-equation-solver-suites-matlab-r-julia-python-c-fortran/) ## Example Images <img src="https://raw.githubusercontent.com/JuliaDiffEq/DifferentialEquations.jl/master/assets/DifferentialEquations_Example.png" align="middle" />
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