DataDrivenDiffEq.jl - GitLite

KO | EN

gitlite — search

#python #react #llm #go #android #rust #deep-learning #nodejs #windows #cli #kubernetes #awesome-list #security #mcp

DataDrivenDiffEq.jl

(★ 430)

Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization

파일 탐색기

최종 버전 다운로드 (.zip)

- - CI.yml
  - DependabotAutoMerge.yml
  - DocPreviewCleanup.yml
  - Documentation.yml
  - Downgrade.yml
  - DowngradeSublibraries.yml
  - Downstream.yml
  - FormatCheck.yml
  - RunicSuggestions.yml
  - SpellCheck.yml
  - SublibraryCI.yml
  - TagBot.yml
- dependabot.yml
- - basis.md
  - citations.md
  - developer_api.md
  - index.md
  - problems.md
  - solutions.md
  - utils.md
- make.jl
- pages.jl
- Project.toml
- - LICENSE.md
  - Project.toml
  - README.md
- - LICENSE.md
  - Project.toml
  - README.md
- - LICENSE.md
  - Project.toml
  - README.md
- - LICENSE.md
  - Project.toml
  - README.md
- - build_function.jl
  - type.jl
  - utils.jl
- - set.jl
  - type.jl
- - basis_generators.jl
  - build_basis.jl
  - collocation.jl
  - common_options.jl
  - data_processing.jl
  - plot_recipes.jl
  - utils.jl
- commonsolve.jl
- DataDrivenDiffEq.jl
- difference.jl
- precompilation.jl
- solution.jl
- - basis.jl
  - commonsolve.jl
  - developer_api.jl
  - generators.jl
  - implicit_basis.jl
  - problem.jl
  - samplers.jl
  - solution.jl
  - utils.jl
- - jet_tests.jl
  - Project.toml
  - qa.jl
- runtests.jl
- test_groups.toml
.codecov.yml
.gitignore
.typos.toml
LICENSE.md
LorenzResult.png
Project.toml
README.md

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# DataDrivenDiffEq.jl

[![Join the chat at https://julialang.zulipchat.com #sciml-bridged](https://img.shields.io/static/v1?label=Zulip&message=chat&color=9558b2&labelColor=389826)](https://julialang.zulipchat.com/#narrow/stream/279055-sciml-bridged)
[![Global Docs](https://img.shields.io/badge/docs-SciML-blue.svg)](https://docs.sciml.ai/DataDrivenDiffEq/stable/)
[![DOI](https://zenodo.org/badge/212827023.svg)](https://zenodo.org/badge/latestdoi/212827023)

[![codecov](https://codecov.io/gh/SciML/DataDrivenDiffEq.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/SciML/DataDrivenDiffEq.jl)
[![Build Status](https://github.com/SciML/DataDrivenDiffEq.jl/workflows/CI/badge.svg)](https://github.com/SciML/DataDrivenDiffEq.jl/actions?query=workflow%3ACI)

[![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor%27s%20Guide-blueviolet)](https://github.com/SciML/ColPrac)
[![SciML Code Style](https://img.shields.io/static/v1?label=code%20style&message=SciML&color=9558b2&labelColor=389826)](https://github.com/SciML/SciMLStyle)

DataDrivenDiffEq.jl is a package in the SciML ecosystem for data-driven differential equation
structural estimation and identification. These tools include automatically discovering equations
from data and using this to simulate perturbed dynamics.

For information on using the package,
[see the stable documentation](https://docs.sciml.ai/DataDrivenDiffEq/stable/). Use the
[in-development documentation](https://docs.sciml.ai/DataDrivenDiffEq/dev/) for the version of
the documentation which contains the un-released features.

## Quick Demonstration

```julia
## Generate some data by solving a differential equation
########################################################
using DataDrivenDiffEq
using ModelingToolkit
using OrdinaryDiffEq
using DataDrivenSparse
using LinearAlgebra

# Create a test problem
function lorenz(u, p, t)
    x, y, z = u

ẋ = 10.0 * (y - x)
    ẏ = x * (28.0 - z) - y
    ż = x * y - (8 / 3) * z
    return [ẋ, ẏ, ż]
end

u0 = [1.0; 0.0; 0.0]
tspan = (0.0, 100.0)
dt = 0.1
prob = ODEProblem(lorenz, u0, tspan)
sol = solve(prob, Tsit5(), saveat = dt)

## Start the automatic discovery
ddprob = DataDrivenProblem(sol)

@variables t x(t) y(t) z(t)
u = [x; y; z]
basis = Basis(polynomial_basis(u, 5), u, iv = t)
opt = STLSQ(exp10.(-5:0.1:-1))
ddsol = solve(ddprob, basis, opt, options = DataDrivenCommonOptions(digits = 1))
println(get_basis(ddsol))
```

```
Explicit Result
Solution with 3 equations and 7 parameters.
Returncode: success
Sparsity: 7.0
L2 Norm Error: 26.7343984476783
AICC: 1.0013570199499398

Model ##Basis#366 with 3 equations
States : x(t) y(t) z(t)
Parameters : 7
Independent variable: t
Equations
Differential(t)(x(t)) = p₁*x(t) + p₂*y(t)
Differential(t)(y(t)) = p₃*x(t) + p₄*y(t) + p₅*x(t)*z(t)
Differential(t)(z(t)) = p₇*z(t) + p₆*x(t)*y(t)

Parameters:
   p₁ : -10.0
   p₂ : 10.0
   p₃ : 28.0
   p₄ : -1.0
   p₅ : -1.0
   p₆ : 1.0
   p₇ : -2.7
```

![](https://raw.githubusercontent.com/SciML/DataDrivenDiffEq.jl/master/LorenzResult.png)