pyMOR - Model Order Reduction with python

pyMOR is a software library for building model order reduction applications with the Python programming language. Implemented algorithms include reduced basis methods for parametric linear and non-linear problems, as well as system-theoretic methods such as balanced truncation or IRKA. All algorithms in pyMOR are formulated in terms of abstract interfaces for seamless integration with external PDE solver packages. Moreover, pure Python implementations of finite element and finite volume discretizations using the NumPy/SciPy scientific computing stack are provided for getting started quickly.

• Getting started
  • Trying it out
  • The thermalblock demo explained
  • Learning more
• Technical Overview
  • Three Central Classes
  • Base Classes
  • Creating Models
  • Parameters
  • Defaults
  • RuleTables
  • The Reduction Process
• Environment Variables
• pyMOR Tutorials
  • Tutorial: Using pyMOR’s discretization toolkit
    • A first equation without parameters
    • Defining boundary conditions
    • Another example
    • Data functions defined from pixel graphics
    • A parametric problem
    • Multiple parameters
    • Parameter-separability
  • Tutorial: Building a Reduced Basis
    • Model setup
    • Computing the snapshot data
    • A trivial reduced basis
    • Strong greedy algorithm
    • Orthonormalization required
    • Proper Orthogonal Decomposition
    • Weak greedy algorithm
  • Tutorial: Reducing a heat equation using balanced truncation
    • Heat equation
    • Discretized model
    • Running balanced truncation
  • Tutorial: Model order reduction with artificial neural networks
    • Feedforward neural networks
    • A non-intrusive reduced order method using artificial neural networks
  • Tutorial: Binding an external PDE solver to pyMOR
    • Defining the PDE solver
    • Binding the solver to Python
    • Using the exported Python classes with pyMOR
    • Putting it all together
• Release Notes
  • pyMOR 2020.1 (July 23, 2020)
    • Release highlights
      • Model order reduction using artificial neural networks
      • New system analysis and linear algebra algorithms
      • Improved parameter handling
      • pyMOR tutorial collection
    • Additional new features
      • Improvements to ParameterFunctionals
      • Extended Newton algorithm
      • initial_guess parameter for apply_inverse
      • manylinux 2010+2014 wheels
      • Debugging improvements
      • WebGL-based visualizations
    • Backward incompatible changes
      • Renamed interface classes
      • Module cleanup
      • State ids removed and caching simplified
      • Further API changes
    • Further notable improvements
  • pyMOR 2019.2 (December 16, 2019)
    • Release highlights
      • Implement new models and reductors more easily
      • Extended VectorArray interface with generic complex number support
      • Improved and extended support for system-theoretic MOR methods
      • Model outputs and parameter sensitivities
    • Additional new features
      • Extended FEniCS bindings
      • Improved greedy algorithms
      • Numerical linear algebra algorithms
      • Support for low-rank operators
      • Improved string representations of pyMOR objects
      • Easier working with immutable objects
      • project and assemble_lincomb are easier to extend
      • Improvements to pyMOR’s discretization toolbox
    • Backward incompatible changes
      • Dropped Python 3.5 support
      • Global RandomState
      • Space id handling
      • Further API Changes
    • Further notable improvements
  • pyMOR 0.5 (January 17, 2019)
    • Release highlights
      • Python 3 support
      • System-theoretic MOR methods
      • NGSolve support
      • New linear algebra algorithms
      • VectorArray improvements
      • RuleTable based algorithms
    • Additional new features
    • Backward incompatible changes
    • Further notable improvements
  • pyMOR 0.4 (September 28, 2016)
    • Release highlights
      • FEniCS and deal.II support
      • Parallelization of pyMOR’s reduction algorithms
      • Support classes for MPI distributed external PDE solvers
      • New reduction algorithms
      • Copy-on-write semantics for VectorArrays
      • Improvements to pyMOR’s discretizaion tookit
      • Caching improvements
    • Additional new features
    • Backward incompatible changes
    • Further improvements
  • pyMOR 0.3 (March 2, 2015)
• Bibliography

API Documentation

• pymor package
  • Subpackages
    • pymor.algorithms package
    • pymor.analyticalproblems package
    • pymor.bindings package
    • pymor.core package
    • pymor.discretizers package
    • pymor.models package
    • pymor.operators package
    • pymor.parallel package
    • pymor.parameters package
    • pymor.reductors package
    • pymor.tools package
    • pymor.vectorarrays package
  • Submodules
    • basic module
    • version module

Demo Applications

• pymordemos package
  • Submodules
    • analyze_pickle module
    • burgers module
    • burgers_ei module
    • delay module
    • elliptic module
    • elliptic2 module
    • elliptic_oned module
    • elliptic_unstructured module
    • fenics_nonlinear module
    • hapod module
    • heat module
    • neural_networks module
    • neural_networks_fenics module
    • parabolic module
    • parabolic_mor module
    • string_equation module
    • thermalblock module
    • thermalblock_adaptive module
    • thermalblock_gui module
    • thermalblock_simple module