Commit 53675e86 authored by Tim Keil's avatar Tim Keil

[docs/tutorials] add references

parent 5a612050
......@@ -872,15 +872,22 @@ the traditional offline/online splitting by only enriching the model along
the path of optimization or (even better) only enrich
the model if the standard error estimator goes above a certain tolerance.
Furthermore, higher order optmization methods with accessible gradient
or hessian make FOM methods take even less steps. Also in this case,
adaptive RB methods still reduce the computational demand of the
optimization method.
A main drawback of the content in this tutorial was that the choice of
the tolerance ``atol`` can not be known a priorily. This shows the need for
the tolerance ``atol`` that has been used to build the RB spaces
cannot be known a priorily. This shows the need for
certified and robust reduced methods.
For some standard literature for faster and robust optimization tools we refer to
`CGT00 <https://epubs.siam.org/doi/book/10.1137/1.9780898719857?mobileUi=0>`__ and
`NW06 <https://link.springer.com/book/10.1007/978-0-387-40065-5>`__.
For recent research on using trust-region methods for MOR of PDE-constrained
optimization problems, we refer to
`YM13 <https://epubs.siam.org/doi/abs/10.1137/120869171>`__,
`QGVW17 <https://epubs.siam.org/doi/abs/10.1137/16M1081981>`__ and
`KMSOV <https://arxiv.org/abs/2006.09297>`__ where for the latter, pyMOR
has been used for the numerical experiments.
Download the code:
:jupyter-download:script:`tutorial_optimization`
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment