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pyMOR developers and contributors

Main developers

  • Linus Balicki, @lbalicki (2020-)
  • René Fritze, @renefritze (2012-2024)
  • Hendrik Kleikamp, @HenKlei (2022-)
  • Petar Mlinarić, @pmli (2016-)
  • Stephan Rave, @sdrave (2012-)
  • Felix Schindler, @ftschindler (2012-)

Contributors

pyMOR 2023.2

  • Steffen Müller, @steff-mueller

    • tutorial for port-Hamiltonian systems
    • interface for dense solvers of positive Riccati equations and dense positive-real Gramians

  • Peter Oehme, @peoe

    • self-documenting help target for Makefile
    • Operator-valued functions in FactorizedTransferFunction
    • H2 norm preset for TransferFunction

  • Art Pelling, @artpelling

    • matrix-free implementation of circulant, Hankel and Toeplitz operators
    • computation of an LTIModel's forced response

pyMOR 2023.1

  • Tim Keil, @TiKeil

    • adaptive trust region algorithm and specific BFGS implementation for PDE-constrained optimization

  • Steffen Müller, @steff-mueller

    • positive-real balanced truncation
    • passivity preserving model reduction via spectral factorization

  • Mohamed Adel Naguib Ahmed, @MohamedAdelNaguib

    • input-output selection in bode_plot function

  • Jonas Nicodemus, @Jonas-Nicodemus

    • port-Hamiltonian IRKA
    • positive-real balanced truncation

  • Peter Oehme, @peoe

    • quadratic functionals and quadratic output keyword for CG discretization
    • simple algebraic operations for parameter values
    • adaptive trust region algorithm and specific BFGS implementation for PDE-constrained optimization

pyMOR 2022.2

  • Tim Keil, @TiKeil

    • Dual-weighted residual (DWR) output estimation for elliptic problems

  • Art Pelling, @artpelling

    • Eigensystem Realization Algorithm

pyMOR 2022.1

  • Patrick Buchfink, @pbuchfink

    • symplectic model order reduction

  • Monica Dessole, @mdessole

    • Navier-Stokes demo using neural networks

  • Hendrik Kleikamp, @HenKlei

    • several additional features for neural networks
    • purely data-driven usage of neural networks without requiring full-order model
    • Navier-Stokes demo using neural networks

  • Peter Oehme, @peoe

    • support for UFL expression conversion

  • Art Pelling, @artpelling

    • functionality to instantiate non-parametric LTIModels with preset attributes
    • support for discrete-time LTI systems, Lyapunov equations and balanced truncation
    • Moebius transformations and continuous/discrete-time conversion of LTI models
    • dedicated Hankel operator class

  • Sven Ullmann, @ullmannsven

    • randomized algorithms for generalized SVD and generalized Hermitian eigenvalue problem

pyMOR 2021.2

  • Tim Keil, @TiKeil

    • Simple output estimation for elliptic and parabolic problems

  • Jonas Nicodemus, @Jonas-Nicodemus

    • dynamic mode decomposition

  • Henrike von Hülsen, h.vonhuelsen@uni-muenster.de

    • dynamic mode decomposition

pyMOR 2021.1

  • Meret B., @meretp

    • modal truncation for model order reduction

  • Hendrik Kleikamp, @HenKlei

    • artificial neural networks for approximation of output quantities

pyMOR 2020.2

  • Tim Keil, @TiKeil

    • energy product in elliptic discretizer
    • rename estimate --> estimate_error and estimator -> error_estimator
    • avoid nested Product and Lincomb Functionals and Functions
    • linear optimization (dual solution, sensitivities, output gradient)

  • Hendrik Kleikamp, @HenKlei

    • artificial neural networks for instationary problems

pyMOR 2020.1

  • Linus Balicki, @lbalicki

    • implicitly restarted Arnoldi method for eigenvalue computation in algorithms.eigs
    • subspace accelerated dominant pole algorithm in algorithms.samdp

  • Tim Keil, @TiKeil

    • second order derivatives for parameters
    • speed up of LincombOperators
    • add LincombParameterFunctional
    • product rule for ProductParameterFunctional
    • BaseMaxThetaParameterFunctional

  • Hendrik Kleikamp, @HenKlei

    • Armijo line search algorithm
    • artificial neural networks for model order reduction

  • Luca Mechelli, @mechiluca

    • speed up of LincombOperators

pyMOR 2019.2

  • Linus Balicki, @lbalicki

    • low-rank RADI method for Riccati equations in algorithms.lrradi
    • improve projection shifts for low-rank ADI method for Lyapunov equations

  • Dennis Eickhorn, @deneick

    • randomized range approximation algorithms in algorithms.randrangefinder
    • fixed complex norms in vectorarrays.interfaces

  • Tim Keil, @TiKeil

    • partial derivatives for parameters d_mu
    • affine decomposition of robin operator and rhs functionals

pyMOR 0.5

  • Linus Balicki, @lbalicki

    • low-rank ADI method using projection shifts for Lyapunov equations in algorithms.lyapunov

  • Julia Brunken, @JuliaBru

    • support for advection, reaction terms and Robin boundary data in ParabolicProblem

  • Christoph Lehrenfeld, @schruste

    • contributions to NGSolve wrappers
    • NGSolve model in thermalblock_simple.py

pyMOR 0.4

  • Andreas Buhr, @andreasbuhr

    • ability to rescale colorbar in each frame
    • SelectionOperator
    • support for advection and reaction terms in finite element discretizations
    • improved Robin boundary condition support

  • Michael Laier, @michaellaier

    • PolygonalDomain, CircularSectorDomain, DiscDomain
    • pymor.domaindiscretizers.gmsh
    • ParabolicProblem, discretize_parabolic_cg, discretize_parabolic_fv
    • reductors.parabolic
    • reductors.residual.reduce_implicit_euler_residual
    • pymordemos.parabolic, pymordemos.parabolic_mor
    • ProductParameterFunctional

  • Falk Meyer, falk.meyer@uni-muenster.de

    • pymor.discretizers.disk

  • Petar Mlinarić, @pmli

    • complex number support for NumpyVectorArray and NumpyMatrixOperator
    • BlockOperator and BlockDiagonalOperator

  • Michael Schaefer, @michaelschaefer

    • Robin boundary condition support for pymor.operators.cg

pyMOR 0.3

  • Andreas Buhr, @andreasbuhr

    • improved PIL compatibility for BitmapFunction
    • improvements to Gram-Schmidt algorithm

  • Lucas Camphausen, lucascamp@web.de

    • bilinear finite elements

  • Michael Laier, @michaellaier

    • finite volume diffusion operator

  • Michael Schaefer, @michaelschaefer

    • new 'columns' parameter for PatchVisualizer

pyMOR 0.2

  • Andreas Buhr, @andreasbuhr

    • reiteration procedure in Gram-Schmidt algorithm for improved numerical stability

  • Michael Laier, @michaellaier

    • documentation improvements

Detailed information

Detailed contribution information can be obtained from the revision history of pyMOR's git repository.