Left column: Upper and lower bounds for the relative H∞ reduction error when using balanced truncation. Middle column: Fullorder model snapshot at time t = 10 and its reconstruction error using a reduced-order model of order 10. Right column: The full-order and reduced-order models' outputs and the error.

Left column: Upper and lower bounds for the relative H∞ reduction error when using balanced truncation. Middle column: Fullorder model snapshot at time t = 10 and its reconstruction error using a reduced-order model of order 10. Right column: The full-order and reduced-order models' outputs and the error.

Source publication
Article
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This paper shows recent developments in pyMOR, in particular the addition of system‐theoretic methods. All methods are implemented using pyMOR's abstract interfaces, which allows the application to partial differential equation (PDE) models implemented with third‐party libraries. We demonstrate this by applying balanced truncation to a PDE model di...

Contexts in source publication

Context 1
... element discretization, using FEniCS, leads to a system of the form (1) with n = 12 296, m = 1, and p = 1. The top-middle plot in Figure 1 shows the state of the full-order model at t = 10 when the input is u(t) = sin( π 3 t) 2 . The average run time is about 8 seconds on a laptop (model: Vaio VJS132C11L; processor: Intel c Core TM i7-8550U CPU @ 1.80 GHz, 4 cores, 8 logical processors, hyper-threading activated; RAM: 8 GB; cache: 8 MB). ...
Context 2
... applied BT (using pyMOR 0.5) to get a ROM of order 10, with a run time of about 7 seconds. Based on Hankel singular values, we find the relative H ∞ -error lies in the interval [7.27 × 10 −5 , 1.23 × 10 −4 ], shown also in the left plot of Figure 1. The relative H 2 -error can be computed and its value is 7.37 × 10 −3 . ...
Context 3
... relative H 2 -error can be computed and its value is 7.37 × 10 −3 . In the bottom row of Figure 1, we see the state and output errors of the ROM. As expected, we see that the output is approximated better than the state. ...

Citations

... Since MOR methods work on top of existing ODE/PDE solvers, their implementation is often non-trivial and requires knowledge of both the given solver and the MOR method to be implemented. pyMOR (Milk et al. (2016); Balicki et al. (2019); Mlinarić et al. (2021), https://www.pymor.org) is a free and open source MOR library for the Python programming language which facilitates the integration of MOR methods with highperformance solvers by expressing MOR algorithms via operations on simple solver interface classes. ...
... Other benchmarks have rather limited parameter dimension, i.e. they feature only scalar or at most two-dimensional parameters. A very common feature among the benchmarks in the Wiki is that essentially all of them are matrix-based, giving easy access for MATLAB ® -based solvers, but at the same time making it difficult for packages like pyMOR [17,14,3] to show their full flexibility. Therefore, the new benchmark introduced in this chapter has a few features addressing exactly these problems. ...
Preprint
In this contribution we aim to satisfy the demand for a publicly available benchmark for parametric model order reduction that is scalable both in degrees of freedom as well as parameter dimension.