A NLPModel API for optimization problems with PDE-constraints
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1 Year Ago
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October 2020


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PDENLPModels specializes the NLPModel API to optimization problems with partial differential equations in the constraints. The package relies on Gridap.jl for the modeling and the computation of the derivatives. Find tutorials for using Gridap here.

We consider optimization problems of the form: Find functions $(y,u): Y \times U \rightarrow ℜⁿ \times ℜⁿ$ and $κ \in ℜⁿ$ satisfying

$$ \begin{equation} \begin{array}{cl} \min_{\kappa,y,u} & \int_\Omega f(\kappa,y,u) d\Omega \\ \text{ s.t. } & y \text{ solution of a } PDE(\kappa,u)=0, \\ & lcon \leq c(\kappa,y,u) \leq ucon, \\ & lvar \leq (\kappa,y,u) \leq uvar,\\ \end{array} \end{equation} $$

We refer to the the repository PDEOptimizationProblems for examples of problems of different types: calculus of variations, optimal control problem, PDE-constrained problems, and mixed PDE-contrained problems with both function and algebraic unknowns.


] add PDENLPModels

The current version of PDENLPModels relies on Gridap v0.15.5.


$$ \begin{equation} \min_{y \in H^1_0,u \in H^1} \frac{1}{2} \int_{\Omega} |y(x) - y_d(x)|^2dx + \frac{\alpha}{2} \int_{\Omega} |u|^2 \quad \text{ s.t. } -\Delta y = u + h, \text{ for } x \in \Omega, y = 0 \text{ for } x \in \partial \Omega, \end{equation} $$

where $y_d(x) = -x_1^2$, $h(x) = 1$ and $\alpha = 10^{-2}$.

using Gridap, PDENLPModels

  # Definition of the domain
  n = 100
  domain = (-1, 1, -1, 1)
  partition = (n, n)
  model = CartesianDiscreteModel(domain, partition)

  # Definition of the spaces:
  valuetype = Float64
  reffe = ReferenceFE(lagrangian, valuetype, 2)
  Xpde = TestFESpace(model, reffe; conformity = :H1, dirichlet_tags = "boundary")
  y0(x) = 0.0
  Ypde = TrialFESpace(Xpde, y0)

  reffe_con = ReferenceFE(lagrangian, valuetype, 1)
  Xcon = TestFESpace(model, reffe_con; conformity = :H1)
  Ycon = TrialFESpace(Xcon)

  # Integration machinery
  trian = Triangulation(model)
  degree = 1= Measure(trian, degree)

  # Objective function:
  yd(x) = -x[1]^2
  α = 1e-2
  function f(y, u)
    (0.5 * (yd - y) * (yd - y) + 0.5 * α * u * u) *end

  # Definition of the constraint operator
  ω = π - 1 / 8
  h(x) = -sin* x[1]) * sin* x[2])
  function res(y, u, v)
    ((v) ⊙ (y) - v * u - v * h) *end

  # initial guess
  npde = num_free_dofs(Ypde)
  ncon = num_free_dofs(Ycon)
  xin = zeros(npde + ncon)

  nlp = GridapPDENLPModel(xin, f, trian, Ypde, Ycon, Xpde, Xcon, res, name = "Control elastic membrane")


Migot, T., Orban D., & Siqueira A. S. PDENLPModels.jl: A NLPModel API for optimization problems with PDE-constraints Journal of Open Source Software 7(80), 4736 (2022). 10.21105/joss.04736

Badia, S., & Verdugo, F. Gridap: An extensible Finite Element toolbox in Julia. Journal of Open Source Software, 5(52), 2520 (2020). 10.21105/joss.02520

How to Cite

If you use PDENLPModels.jl in your work, please cite using the format given in CITATION.cff.

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