# \[ANN\] DataDrivenDiffEq.jl - Overhauled in 0.6.0

**URL:** <https://discourse.julialang.org/t/ann-datadrivendiffeq-jl-overhauled-in-0-6-0/62199>\
**Category:** Package Announcements\
**Tags:** symbolic, sciml, modelingtoolkit\
**Created:** [June 1, 2021, 3:17pm UTC](https://discourse.julialang.org/t/ann-datadrivendiffeq-jl-overhauled-in-0-6-0/62199 "2021-06-01T15:17:46Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![Julius\_Martensen](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/julius_martensen/32/17077_2.png) [@Julius\_Martensen](https://discourse.julialang.org/u/Julius_Martensen)\
**Post date:** [June 1, 2021, 3:17pm UTC](https://discourse.julialang.org/t/ann-datadrivendiffeq-jl-overhauled-in-0-6-0/62199/1 "2021-06-01T15:17:46Z")

</div>

I’d like to finally and officially announce the [DataDrivenDiffEq.jl](https://github.com/SciML/DataDrivenDiffEq.jl), a package in the SciML ecosystem for the structural estimation and inference of nonlinear differential equations.

The main features include:

- Dynamic Mode Decomposition ( in all of the flavours for discrete, extended, continuous and controlled systems )
- Sparse Identification of Nonlinear Dynamics ( explicit + implicit with or without controls)
- Tight integration with ModelingToolkit and the SciML ecosystem

This is already the `v0.6.0` release, but really the first one which _feels_ like a sufficient release for me personally. A quick copy and paste example, taken from the Readme:

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

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, progress = true)

## Start the automatic discovery 
# This just collects the state and derivative information
ddprob = ContinuousDataDrivenProblem(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, normalize = true)
print(ddsol, Val{true})

```

which results in

```julia
Explicit Result
Solution with 3 equations and 7 parameters.
Returncode: sucess
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

```

I am looking forward to getting issues, feature and maybe even pull requests 😉