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Titlebook: Computational Methods for Solids and Fluids; Multiscale Analysis, Adnan Ibrahimbegovic Book 2016 Springer International Publishing Switzerl

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51#
發(fā)表于 2025-3-30 10:53:22 | 只看該作者
978-3-319-80254-1Springer International Publishing Switzerland 2016
52#
發(fā)表于 2025-3-30 15:55:28 | 只看該作者
https://doi.org/10.1007/978-3-642-54851-2level of the city and the urban district is particularly difficult to appreciate, because it involves huge geometries that must however be precisely detailed and finely meshed. It has become very important to simulate on this scale, principally energy exchanges, because the urban environment, with i
53#
發(fā)表于 2025-3-30 20:35:18 | 只看該作者
Xu Qiao,Takanori Igarashi,Yen-Wei Chen solid or structure. It can be also applied for complete failure computation of a solid or structure that is performed by using a material failure model. In this work, we applied it for computations with the embedded-discontinuity finite elements that use rigid-plastic cohesive laws with softening t
54#
發(fā)表于 2025-3-30 21:05:32 | 只看該作者
55#
發(fā)表于 2025-3-31 01:55:29 | 只看該作者
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發(fā)表于 2025-3-31 07:45:26 | 只看該作者
57#
發(fā)表于 2025-3-31 09:10:45 | 只看該作者
Subspace Identification (1) — ORT fields (mechanical, chemical, electrical), occur at different scales, can appear as uncertain, and are nonlinear. Interacting with complex materials thus calls for developing nonlinear computational approaches where multi-scale techniques that grasp key phenomena at the relevant scale need to be mi
58#
發(fā)表于 2025-3-31 13:27:51 | 只看該作者
Stochastic Realization Theory (2) model and the structure. Problem is addressed on the general level but after discretization and is formulated as a relationship between relevant parameters of the structure and its model. Scaling matrices in parameter and in measurement space are determined.
59#
發(fā)表于 2025-3-31 20:28:18 | 只看該作者
Stochastic Realization Theory (2)en evaluating the non-linear Finite Element Model (FEM), simulations involving contact/friction and material non linearity on such small perturbations of the input data, a numerical noise alters the output data and distorts the statistical quantities and potentially inhibit the training of Uncertain
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