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pro vyhledávání: '"Díez, Pedro"'
A posteriori reduced-order models (ROM), e.g. based on proper orthogonal decomposition (POD), are essential to affordably tackle realistic parametric problems. They rely on a trustful training set, that is a family of full-order solutions (snapshots)
Externí odkaz:
http://arxiv.org/abs/2312.14756
Autor:
Larion, Ygee, Massart, Thierry J., Díez, Pedro, Chen, Guangjing, Seetharam, Suresh, Zlotnik, Sergio
Publikováno v:
In Finite Elements in Analysis & Design 15 October 2024 239
We present a general framework to compute upper and lower bounds for linear-functional outputs of the exact solutions of the Poisson equation based on reconstructions of the field variable and flux for both the primal and adjoint problems. The method
Externí odkaz:
http://arxiv.org/abs/2106.10945
The use of Internet of Things (IoT) technologies is becoming a preferred solution for the assessment of tailings dams' safety. Real-time sensor monitoring proves to be a key tool for reducing the risk related to these ever-evolving earth-fill structu
Externí odkaz:
http://arxiv.org/abs/2106.02687
Reduced-order models are essential tools to deal with parametric problems in the context of optimization, uncertainty quantification, or control and inverse problems. The set of parametric solutions lies in a low-dimensional manifold (with dimension
Externí odkaz:
http://arxiv.org/abs/2104.13765
Uncertainty Quantification (UQ) is a booming discipline for complex computational models based on the analysis of robustness, reliability and credibility. UQ analysis for nonlinear crash models with high dimensional outputs presents important challen
Externí odkaz:
http://arxiv.org/abs/2103.16202
Uncertainty Quantification (UQ) is a key discipline for computational modeling of complex systems, enhancing reliability of engineering simulations. In crashworthiness, having an accurate assessment of the behavior of the model uncertainty allows red
Externí odkaz:
http://arxiv.org/abs/2102.07673
Methodologies for multidimensionality reduction aim at discovering low-dimensional manifolds where data ranges. Principal Component Analysis (PCA) is very effective if data have linear structure. But fails in identifying a possible dimensionality red
Externí odkaz:
http://arxiv.org/abs/2001.01958
Akademický článek
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Publikováno v:
In Finite Elements in Analysis & Design 1 June 2022 203