Výsledky vyhledávání - "Burohman, Azka"

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Kron-based Model-order Reduction of Open Mass-action Kinetics Chemical Reaction Networks

Autor: Negara, Mohamad Agung Prawira, Burohman, Azka Muji, Jayawardhana, Bayu

We propose a Kron-based model-order reduction method for mass-action kinetics chemical reaction networks (CRN) with constant inflow and proportional outflow. The reduced-order models preserve the CRN structure and we establish that the resulting redu

Externí odkaz: http://arxiv.org/abs/2203.16135

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From data to reduced-order models via generalized balanced truncation

Autor: Burohman, Azka Muji, Besselink, Bart, Scherpen, Jacquelien M. A., Camlibel, M. Kanat

This paper proposes a data-driven model reduction approach on the basis of noisy data. Firstly, the concept of data reduction is introduced. In particular, we show that the set of reduced-order models obtained by applying a Petrov-Galerkin projection

Externí odkaz: http://arxiv.org/abs/2109.11685

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From data to reduced-order models via moment matching

Autor: Burohman, Azka Muji, Besselink, Bart, Scherpen, Jacquelien M. A., Camlibel, M. Kanat

A new method for data-driven interpolatory model reduction is presented in this paper. Using the so-called data informativity perspective, we define a framework that enables the computation of moments at given (possibly complex) interpolation points

Externí odkaz: http://arxiv.org/abs/2011.00150

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Informativity for data-driven model reduction through interpolation

Autor: Burohman, Azka Muji, Besselink, Bart, Scherpen, Jacquelien M. A., Camlibel, M. Kanat

A method for data-driven interpolatory model reduction is presented in this extended abstract. This framework enables the computation of the transfer function values at given interpolation points based on time-domain input-output data only, without e

Externí odkaz: http://arxiv.org/abs/2005.04427

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Dissipativity-Preserving Reduced-Order Modeling from Data

Autor: Burohman, Azka, Besselink, Bart, Scherpen, Jacquelien M.A., Camlibel, Kanat

Publikováno v: 2023 SIAM Conference on Computational Science and Engineering(CSE23)

Reduced-order modeling from data with dissipativity preservation is discussed in this talk. Employing the data informativity framework, the dissipativity of all systems consistent with noisy data can be characterized by a data-based linear matrix ine

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=narcis______::a4e12beaadb07861c68ba8f65844815d
https://research.rug.nl/en/publications/eaea5847-73fd-461c-a72f-f3e4a71f85ad

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Kron Reduction of Open Chemical Reaction Network: Zero-Moment Matching and a Priori Error Bound Via Generalized Gramian

Autor: Prawira Negara, Agung, Burohman, Azka, Jayawardhana, Bayu

Publikováno v: 25th International Symposium on Mathematical Theory of Networks and Systems

In this extended abstract, we propose a Kron-based model reduction method for open chemical reaction networks (CRN) with constant inflow and proportional outflow, which guarantees the preservation of network structures and interlacing property of the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=narcis______::3d68926da9eec9e0f0eba46c0677ca63
https://research.rug.nl/en/publications/b74d3032-0758-4127-8c65-42c4f1d40fa9

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Kron Reduction of Open Chemical Reaction Network: Zero-Moment Matching and a Priori Error Bound Via Generalized Gramian (I)

Autor: Prawira Negara, Agung, Burohman, Azka, Jayawardhana, Bayu, Baumann, Michael Heinrich, Grüne, Lars, Jacob, Birgit Jacob, Worthmann, Karl

Publikováno v: Proceedings of the 25th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2022)

We propose a Kron-based model reduction method for open chemical reaction networks with constant inflow and proportional outflow, which guarantees the preservation of network structures and interlacing property of the reduced-order model. We further

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=narcis______::7957e560a152016bf840a80c3483dd52
https://research.rug.nl/en/publications/7ae065eb-2002-4e04-9d5c-e4e7894e0b77

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Reduced-Order Models from Data Via Generalized Balanced Truncation (I): (Extended abstract)

Autor: Burohman, Azka, Besselink, Bart, Scherpen, Jacquelien M.A., Camlibel, Kanat

Publikováno v: 25th International Symposium on Mathematical Theory of Networks and Systems

This extended abstract proposes a data-driven model reduction approach on the basis of noisy data. Firstly, the concept of data reduction is introduced. In particular, we show that the set of reduced-order models obtained by applying a Petrov-Galerki

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=narcis______::547cf81f5936f7e27d0c9a5e38a0b48f
https://research.rug.nl/en/publications/655adfc2-c843-467a-8c67-c122b9a09941

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