Pymor

and Meret Behrens.

Read the [release notes](https://docs.pymor.org/2021-1-0/release_notes/all.html)
for more details.

We are proud to announce the release of pyMOR 2021.1.0! This release includes
several new reductors for LTI systems. In particular, methods for reducing and
analyzing unstable systems have been added. ANNs can now be used in order to
directly approximate output quantities. Furthermore, it is now possible to
work with time-dependent parameters in pyMOR.

Over 700 single commits have entered this release. For a full list of changes
see [here](https://github.com/pymor/pymor/compare/2020.2.x...2021.1.x).

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.

Highlights of this release are:
- Parameter derivatives of solutions and outputs
- Neural network reductor for non-stationary problems
- New tutorials

You can read the full release notes at https://docs.pymor.org/2020.2.0/release_notes/all.html

- for the PyMESS bindings we now ensure solve_lyap_dense returns a NumPy array
- to avoid setup problems, and following NumPy we require setuptools < 49.2.0
- improved consistency in Newton and line search logging output

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.

Highlights of this release are:
- Non-intrusive model order reduction using artificial neural networks.
- The subspace accelerated dominant pole algorithm (SAMDP).
- The implicitly restarted Arnoldi method for eigenvalue computation.
- Parameter handling in pyMOR has been simplified.
- A new series of hands-on tutorials.

You can read the full release notes at https://docs.pymor.org/2020.1.1/release_notes.html