کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
13454150 | 1844259 | 2019 | 13 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Incremental dynamic mode decomposition: A reduced-model learner operating at the low-data limit
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موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مهندسی (عمومی)
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
The present work aims at proposing a new methodology for learning reduced models from a small amount of data. It is based on the fact that discrete models, or their transfer function counterparts, have a low rank and then they can be expressed very efficiently using few terms of a tensor decomposition. An efficient procedure is proposed as well as a way for extending it to nonlinear settings while keeping limited the impact of data noise. The proposed methodology is then validated by considering a nonlinear elastic problem and constructing the model relating tractions and displacements at the observation points.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Comptes Rendus Mécanique - Volume 347, Issue 11, November 2019, Pages 780-792
Journal: Comptes Rendus Mécanique - Volume 347, Issue 11, November 2019, Pages 780-792
نویسندگان
Agathe Reille, Nicolas Hascoet, Chady Ghnatios, Amine Ammar, Elias Cueto, Jean Louis Duval, Francisco Chinesta, Roland Keunings,