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pyMOR
pymor
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f5ef7d71
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Dec 10, 2020
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René Fritze
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[docs] releasenotes 2020.2: final edits
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docs/source/release_notes/2020.2.rst
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f5ef7d71
pyMOR 2020.2 (December
?
, 2020)
pyMOR 2020.2 (December
10
, 2020)
--------------------------------
We are proud to announce the release of pyMOR 2020.2! This release extends pyMOR's
support for non-intrusive model reduction via artificial neural networks to
non-stationary models. Built-in support for computing parameter sensitivies
non-stationary models. Built-in support for computing parameter sensitivi
ti
es
simplifies the use of pyMOR in PDE-constrained optimization applications.
pyMOR's documentation has been extended by three new tutorials, and all tutorial
code can now easily be executed using `binder <https://mybinder.org>`_.
Over
40
0 single commits have entered this release. For a full list of changes
Over
52
0 single commits have entered this release. For a full list of changes
see `here <https://github.com/pymor/pymor/compare/2020.1.x...2020.2.x>`__.
pyMOR 2020.2 contains contributions by Tim Keil and Hendrik Kleikamp.
...
...
@@ -21,22 +21,23 @@ Release highlights
Parameter derivatives of solutions and outputs
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
In `[#1110] <https://github.com/pymor/pymor/pull/1110>`_
, some
tools for
In `[#1110] <https://github.com/pymor/pymor/pull/1110>`_ tools for
PDE-constrained parameter optimization were added. These include parameter derivatives
of the solutions and the output of a |Model|. In particular,
:meth:`~pymor.models.interface.Model.solve_d_mu` can be used to compute partial
:meth:`~pymor.models.interface.Model.solve_d_mu` can
now
be used to compute partial
parameter derivatives. Moreover, :meth:`~pymor.models.interface.Model.output_d_mu`
can be used to compute the parameter gradient of the output using the
derivatives of the solutions. For a |StationaryModel| and a linear output, an
adjoint variable can instead be used to speedup the computation of the gradient, see :meth:`~pymor.models.basic.StationaryModel._compute_output_d_mu`.
derivatives of the solutions. Alternatively, for a |StationaryModel| and a linear output, an
adjoint variable can be used to speed up the computation of the gradient
(see :meth:`~pymor.models.basic.StationaryModel._compute_output_d_mu`).
Neural network reductor for non-stationary problems
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
A reductor based on neural networks which deals with non-stationary problems was
added in `[#1120] <https://github.com/pymor/pymor/pull/1120>`_. The
approach
is an
extension of the approach for stationary problems
that was already implemented in
pyMOR.
Here, the time component is treated as an ordinary parameter. The usage of
added in `[#1120] <https://github.com/pymor/pymor/pull/1120>`_. The
implementation
is an
extension of the
already existing
approach for stationary problems
in pyMOR.
Here, the time component is treated as an ordinary parameter. The usage of
the newly introduced reductor is presented in a corresponding demo where a Burgers'
type equation is solved. As in the stationary case, the implementation allows for
several customizations regarding the network architecture and training parameters.
...
...
@@ -49,22 +50,22 @@ randomly before splitting it into training and validation set
New tutorials
~~~~~~~~~~~~~
A new tutorial on :doc:`using pyMOR for accelerating the solution of linear PDE-constrained
optimization problems <../tutorial_optimization>` has been added
`[#1205] <https://github.com/pymor/pymor/pull/1205>`_.
The tutorial showcases the new features added in
`[#1
110
] <https://github.com/pymor/pymor/pull/1
110
>`_
and also discusses general questions on using model order reduction for this class
of optimization problems.
optimization problems <../tutorial_optimization>` has been added
with
`[#1
205
] <https://github.com/pymor/pymor/pull/1
205
>`_
.
This tutorial showcases the new features added in `[#1110] <https://github.com/pymor/pymor/pull/1110>`_
and also discusses general questions on using model order reduction for a class
of optimization problems.
The tutorial :doc:`'Projecting a Model' <../tutorial_projection>` explains how pyMOR build
s
an
The tutorial :doc:`'Projecting a Model' <../tutorial_projection>` explains how
to use
pyMOR
to
build an
online-efficient reduced order model via (Petrov-)Galerkin projection onto a given reduced space
`[#1084] <https://github.com/pymor/pymor/pull/1084>`_.
A
part from a
mathematical
introduc
tion, the user is introduced to
som
e core elements
A
longside the
mathematical
founda
tion, the user is introduced to
th
e core elements
of
pyMOR's internal architecture that realize the projection.
A tutorial on :doc:`linear time-invariant systems <../tutorial_lti_systems>` was added
and the existing :doc:`balanced truncation tutorial <../tutorial_bt>` was appropriately simplified
`[#1141] <https://github.com/pymor/pymor/pull/1141>`_.
All tutorials now include a 'launch binder' button which allows to directly
All tutorials now include a 'launch binder' button which allows to directly
run the tutorial code in the web browser `[#1181] <https://github.com/pymor/pymor/pull/1181>`_.
In order to consolidate our documentation all remaining Jupyter notebooks from the `notebooks/`
...
...
@@ -90,7 +91,7 @@ Iterable VectorArrays
~~~~~~~~~~~~~~~~~~~~~
|VectorArrays| became iterable sequences with
`[#1068] <https://github.com/pymor/pymor/pull/1068>`_, i.e.,
`for v in V` can be used to work on individual vectors
`for v in V` can be used to work on individual vectors
(i.e. |VectorArray| views of length 1) when needed.
...
...
@@ -119,8 +120,8 @@ Backward incompatible changes
Updated Model interface
~~~~~~~~~~~~~~~~~~~~~~~
To make the simultaneous computation of multiple |Model| output quantities such internal state,
output, or error estimates more efficient better customizable a :meth:`~pymor.models.interface.Model.compute`
To make the simultaneous computation of multiple |Model| output quantities such
as
internal state,
output, or error estimates more efficient
and
better customizable a :meth:`~pymor.models.interface.Model.compute`
method was added to the |Model| interface which is now responsible for the computation of all
relevant data that can be gathered from the simulation of a |Model|
`[#1113] <https://github.com/pymor/pymor/pull/1113>`_.
...
...
@@ -135,7 +136,7 @@ The old method is deprecated and will be removed in the next release.
Further, to simplify interoperability with third-party packages,
the model outputs, i.e., the results of :meth:`~pymor.models.interface.output`,
are no
t
longer generic |VectorArrays|, but NumPy arrays.
are no longer generic |VectorArrays|, but NumPy arrays.
For consistency, `input_space` and `output_space` were removed and
`input_dim` and `output_dim` were renamed to `dim_input` and `dim_output`
in :class:`~pymor.models.iosys.InputOutputModel`
...
...
@@ -186,8 +187,9 @@ renamed to `ComponentProjectionOperator`, `ConcatenationOperator` and
Minimal pip and Manylinux wheel version
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
In order to reduce special casing and infrastructure investment needed for older pip versions we decided to
bump the minimal version to 19.0 (released Jan '19) and decided to no longer publish manylinux1 wheels.
In order to reduce special casing and infrastructure investment needed for
maintaining compatibility with older versions we decided to
increase the minimal required pip version to 19.0 (released Jan '19) and decided to no longer publish manylinux1 wheels.
Pip 19.0 already understands the Manylinux 2010 tag, which going further is the oldest platform we will ship wheels
for.
...
...
René Fritze
@r_milk01
mentioned in commit
f7c50832
·
Dec 10, 2020
mentioned in commit
f7c50832
mentioned in commit f7c50832eafca3099b44971bf95333dd7575b7a2
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