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Titlebook: Reduction, Approximation, Machine Learning, Surrogates, Emulators and Simulators; RAMSES Gianluigi Rozza,Giovanni Stabile,Marta D‘Elia Book

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發(fā)表于 2025-3-21 19:30:24 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Reduction, Approximation, Machine Learning, Surrogates, Emulators and Simulators
副標題RAMSES
編輯Gianluigi Rozza,Giovanni Stabile,Marta D‘Elia
視頻videohttp://file.papertrans.cn/825/824569/824569.mp4
概述Provides guidelines for the most promising approaches for near-future research.Synergistic exchanges between the mathematics and data science communities.Latest knowledge and results on emerging topic
叢書名稱Lecture Notes in Computational Science and Engineering
圖書封面Titlebook: Reduction, Approximation, Machine Learning, Surrogates, Emulators and Simulators; RAMSES Gianluigi Rozza,Giovanni Stabile,Marta D‘Elia Book
描述.This volume is focused on the review of recent algorithmic and mathematical advances and the development of new research directions for Mathematical Model Approximations via RAMSES (Reduced order models, Approximation theory, Machine learning, Surrogates, Emulators, Simulators) in the setting of parametrized partial differential equations also with sparse and noisy data in high-dimensional parameter spaces.. .The book is a valuable resource for researchers, as well as masters and Ph.D students..
出版日期Book 2024
關(guān)鍵詞Model order reduction; Machine learning; Approximation of PDEs; Surrogates; Emulators; Simulators
版次1
doihttps://doi.org/10.1007/978-3-031-55060-7
isbn_softcover978-3-031-55062-1
isbn_ebook978-3-031-55060-7Series ISSN 1439-7358 Series E-ISSN 2197-7100
issn_series 1439-7358
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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發(fā)表于 2025-3-21 22:04:21 | 只看該作者
,Data Assimilation with?Deep Neural Nets Informed by?Nudging,Lorenz 63 model for both the continuous and discrete in time models. These results can be directly coupled with estimates for DNNs (whenever available), to derive the overall approximation error estimates of the proposed algorithm.
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發(fā)表于 2025-3-22 01:34:05 | 只看該作者
,Reduced Order Modelling of?Nonaffine Problems on?Parameterized NURBS Multipatch Geometries,a parameterized setting, where multiple evaluations are performed for a given set of geometrical parameters, and employ the Static Condensation Reduced Basis Element (SCRBE) method. At the common interface between adjacent patches a static condensation procedure is employed, whereas in the interior
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,Reduced Models with?Nonlinear Approximations of?Latent Dynamics for?Model Premixed Flame Problems,the training regime and in regimes where traditional static reduced models, which keep reduced spaces fixed over time and so provide only linear approximations of latent dynamics, fail to make meaningful predictions.
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發(fā)表于 2025-3-22 16:58:35 | 只看該作者
Davide Calabrò,Massimiliano Lupo Pasini,Nicola Ferro,Simona Perotto
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發(fā)表于 2025-3-23 01:09:56 | 只看該作者
Julien Genovese,Francesco Ballarin,Gianluigi Rozza,Claudio Canuto
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發(fā)表于 2025-3-23 05:50:44 | 只看該作者
Anna Ivagnes,Giovanni Stabile,Andrea Mola,Gianluigi Rozza,Traian Iliescu
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