https://github.com/mathopt/dynamicoed.jl

Optimal experimental design of ODE and DAE systems in julia
https://github.com/mathopt/dynamicoed.jl

mixed-integer-programming optimal-control optimal-experimental-design sciml

Last synced: about 1 month ago
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Optimal experimental design of ODE and DAE systems in julia

• Host: GitHub
• URL: https://github.com/mathopt/dynamicoed.jl
• Owner: mathopt
• License: mit
• Created: 2023-11-27T07:56:49.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2025-10-03T01:10:46.000Z (2 months ago)
• Last Synced: 2025-10-03T12:52:55.884Z (2 months ago)
• Topics: mixed-integer-programming, optimal-control, optimal-experimental-design, sciml
• Language: Julia
• Homepage: https://mathopt.github.io/DynamicOED.jl/
• Size: 913 KB
• Stars: 11
• Watchers: 1
• Forks: 0
• Open Issues: 12
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# DynamicOED.jl

[![Latest Docs](https://img.shields.io/badge/docs-dev-blue.svg)](https://mathopt.github.io/DynamicOED.jl/dev/)

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[![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor's%20Guide-blueviolet)](https://github.com/SciML/ColPrac)

[![DOI](https://joss.theoj.org/papers/10.21105/joss.06605/status.svg)](https://doi.org/10.21105/joss.06605)

Repository for optimal experimental design for differential equations using optimal control.

`DynamicOED.jl` uses multiple packages of Julia's [SciML](https://sciml.ai/) ecosystem, especially [ModelingToolkit.jl](https://github.com/SciML/ModelingToolkit.jl), [DifferentialEquations.jl](https://github.com/SciML/DifferentialEquations.jl) and [Optimization.jl](https://github.com/SciML/Optimization.jl) to define [optimal experimental design problems using optimal control](https://doi.org/10.1137/110835098).

## Features

+ Currently we support Ordinary Differential Equations and Differential Algebraic Equations.

+ Relaxed and Integer formulations of the underlying problem

+ Unknown initial conditions

+ Continuous and discrete controls (in terms of the variable)

+ Variable (measurement) rates for observed and control variables

+ Custom constraints 

## Example

```julia

using DynamicOED

using ModelingToolkit

using Optimization, OptimizationMOI, Ipopt

# Define the differential equations

@variables t

@variables x(t)=1.0 [description = "State"]

@parameters p[1:1]=-2.0 [description = "Fixed parameter", tunable = true]

@variables obs(t) [description = "Observed", measurement_rate = 10]

D = Differential(t)

@named simple_system = ODESystem([

        D(x) ~ p[1] * x,

    ], tspan = (0.0, 1.0),

    observed = obs .~ [x.^2])

@named oed = OEDSystem(simple_system)

oed = structural_simplify(oed)

# Augment the original problem to an OED problem

oed_problem = OEDProblem(structural_simplify(oed), FisherACriterion())

# Define an MTK Constraint system over the grid variables

optimization_variables = states(oed_problem)

        

constraint_equations = [

      sum(optimization_variables.measurements.w₁) ≲ 3,

]

@named constraint_set = ConstraintsSystem(constraint_equations, optimization_variables, Num[])

# Initialize the optimization problem

optimization_problem = OptimizationProblem(oed_problem, AutoForwardDiff(),

      constraints = constraint_set,

      integer_constraints = false)

# Solven for the optimal values of the observed variables

solve(optimization_problem, Ipopt.Optimizer())

```