Preprint Open Access

Maximum Entropy Snapshot Sampling for Reduced Basis Modelling

Bannenberg, M.W.F.M.; Kasolis, F.; Günther, M.; Clemens, M.


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    "description": "<p>The so-called maximum entropy snapshot sampling method is employed for reducing two nonlinear circuit models. The maximum entropy snapshot sampling directly reduces the number of snapshots by recursively identifying and selecting the snapshots that strictly increase an estimate of the correlation entropy of the considered systems. Reduced bases are then obtained with the orthogonal-triangular decomposition. In the first case study, the resulting overdetermined systems are solved in the least squares sense. In the second case study, the basis is incorporated in a reduced order multirate scheme, whilst the reduction parameter is estimated through an optimality requirement. Numerical experiments verify the performance of the advocated approach, in terms of computational costs and accuracy, relative to an established reduction framework that is based on the singular value decomposition.</p>", 
    "language": "eng", 
    "title": "Maximum Entropy Snapshot Sampling for Reduced Basis Modelling", 
    "license": {
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    "notes": "Preprint BUW-IMACM 20/46, Institute of Mathematical Modelling, Analysis and Computational Mathematics (IMACM), Bergische Universit\u00e4t Wuppertal, October 2020", 
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    "keywords": [
      "Circuit models, entropy, nonlinear model reduction, QR decomposition"
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    "publication_date": "2020-10-20", 
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