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Difference between revisions of "MORB"

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= Model Order Reduction Benchmarker (MORB) =
 
 
A new benchmarking tool for model order reduction is currently under development. It is a demonstrator of the generic benchmark framework developed for Task Area 2 of the Mathematical Research Data Initiative ([https://www.mardi4nfdi.de/about/task-areas MaRDI] and will serve as an example of how to fairly compare different implementations of algorithms across a standardized benchmark set for scientific computing.
 
A new benchmarking tool for model order reduction is currently under development. It is a demonstrator of the generic benchmark framework developed for Task Area 2 of the Mathematical Research Data Initiative ([https://www.mardi4nfdi.de/about/task-areas MaRDI] and will serve as an example of how to fairly compare different implementations of algorithms across a standardized benchmark set for scientific computing.
   
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[[File:Morb2022.pdf|180px]]
 
[[File:Morb2022.pdf|180px]]
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As of January 2023, a MATLAB prototype for linear-time-invariant (LTI) systems is largely operational. See [[File:cdPlayer_n120m2q2.pdf|180px]] and [[File:steelProfile_n1357m7q6.pdf|180px]] for example reports.
   
 
Please check back often for updates.
 
Please check back often for updates.

Revision as of 15:24, 15 February 2023

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A new benchmarking tool for model order reduction is currently under development. It is a demonstrator of the generic benchmark framework developed for Task Area 2 of the Mathematical Research Data Initiative (MaRDI and will serve as an example of how to fairly compare different implementations of algorithms across a standardized benchmark set for scientific computing.

An overview of MaRDI and MORB are provided in the poster, which was presented at Model Reduction and Surrogate Modeling (MORE), 19-23 September 2022, Berlin, Germany.

Morb2022.pdf

As of January 2023, a MATLAB prototype for linear-time-invariant (LTI) systems is largely operational. See CdPlayer n120m2q2.pdf and 180px for example reports.

Please check back often for updates.