EXTRA
发表于 2025-3-21 19:01:00
书目名称Manifold Learning影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0623388<br><br> <br><br>书目名称Manifold Learning影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0623388<br><br> <br><br>书目名称Manifold Learning网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0623388<br><br> <br><br>书目名称Manifold Learning网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0623388<br><br> <br><br>书目名称Manifold Learning被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0623388<br><br> <br><br>书目名称Manifold Learning被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0623388<br><br> <br><br>书目名称Manifold Learning年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0623388<br><br> <br><br>书目名称Manifold Learning年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0623388<br><br> <br><br>书目名称Manifold Learning读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0623388<br><br> <br><br>书目名称Manifold Learning读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0623388<br><br> <br><br>
opprobrious
发表于 2025-3-21 20:59:01
Resources: Software and Tutorials,dustrial usage”. It is the name of a collaborative project that took place from 2018 to 2023, with the objective of developing a standard for a datamodel and basic computational treatment for reduced-order modeling in the French community.
单独
发表于 2025-3-22 02:31:05
Industrial Application: Uncertainty Quantification in Lifetime Prediction of Turbine Blades,turbine blades, generated by the uncertainty of the temperature loading field. A complete reduced-order model workflow is detailed, and the numerical experiments make use of the codes Mordicus and genericROM introduced in Chap. 4.
商店街
发表于 2025-3-22 04:55:10
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Spinous-Process
发表于 2025-3-22 11:40:10
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IVORY
发表于 2025-3-22 15:29:41
https://doi.org/10.1007/978-3-031-52764-7Computational Mechanics; Data Augmentation; Deep Learning; Digital Twining; Dimensionality Reduction; Gen
伴随而来
发表于 2025-3-22 20:41:43
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tympanometry
发表于 2025-3-22 21:14:11
Manifold Learning978-3-031-52764-7Series ISSN 2191-5768 Series E-ISSN 2191-5776
丛林
发表于 2025-3-23 03:17:23
Book‘‘‘‘‘‘‘‘ 2024 to Master and PhD students, as well as to researchers, lecturers and instructors. The aim of the authors is to provide tools for a better understanding and implement reduced order models by using: physics-based models, synthetic data forecast by these models, experimental data and deep learning alg
Aerate
发表于 2025-3-23 05:55:43
Learning Projection-Based Reduced-Order Models,the generalisation of the reduced order model is evaluated in the online step by using a test set of data forecast by the high-fidelity model. The test set aims also to check the computational speedups of the reduced-order model compare to the high-fidelity model.