Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Phaedon-Stelios Koutsourelakis"'
Publikováno v:
Data-Centric Engineering, Vol 5 (2024)
We propose a systematic design approach for the precast concrete industry to promote sustainable construction practices. By employing a holistic optimization procedure, we combine the concrete mixture design and structural simulations in a joint, for
Externí odkaz:
https://doaj.org/article/568bc41eb6624398ab4315f6db63c8c6
Publikováno v:
npj Computational Materials, Vol 8, Iss 1, Pp 1-11 (2022)
Abstract While the forward and backward modeling of the process-structure-property chain has received a lot of attention from the materials’ community, fewer efforts have taken into consideration uncertainties. Those arise from a multitude of sourc
Externí odkaz:
https://doaj.org/article/5d6d7a3e9d3447fc9a82bfc95bc1d736
Autor:
Isabela Coelho Lima, Annika Robens-Radermacher, Thomas Titscher, Daniel Kadoke, Phaedon-Stelios Koutsourelakis, Jörg F. Unger
Publikováno v:
Computational Mechanics. 70:1189-1210
Numerical models built as virtual-twins of a real structure (digital-twins) are considered the future of monitoring systems. Their setup requires the estimation of unknown parameters, which are not directly measurable. Stochastic model identification
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b4204bdbfec8f42519480fadba443647
https://doi.org/10.1007/s00466-022-02214-6
https://doi.org/10.1007/s00466-022-02214-6
Neural Operators offer a powerful, data-driven tool for solving parametric PDEs as they can represent maps between infinite-dimensional function spaces. In this work, we employ physics-informed Neural Operators in the context of high-dimensional, Bay
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0d401e51ae9131904a9447931afd2261
http://arxiv.org/abs/2209.02772
http://arxiv.org/abs/2209.02772
Two of the most significant challenges in uncertainty propagation pertain to the high computational cost for the simulation of complex physical models and the high dimension of the random inputs. In applications of practical interest both of these pr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1b20e86535bb3570c6ff6e38abf57b6e
https://mediatum.ub.tum.de/doc/1537002/document.pdf
https://mediatum.ub.tum.de/doc/1537002/document.pdf
Publikováno v:
Journal of Computational Physics. 394:56-81
Surrogate modeling and uncertainty quantification tasks for PDE systems are most often considered as supervised learning problems where input and output data pairs are used for training. The construction of such emulators is by definition a small dat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ea776fd7d70ad81d2777275c1f40f34f
https://doi.org/10.1016/j.jcp.2019.05.024
https://doi.org/10.1016/j.jcp.2019.05.024
While the forward and backward modeling of the process-structure-property chain has received a lot of attention from the materials’ community, fewer efforts have taken into consideration uncertainties. Those arise from a multitude of sources and th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0ae24daf17c7ba010fa6cd7e8581ef8c
The data-based discovery of effective, coarse-grained (CG) models of high-dimensional dynamical systems presents a unique challenge in computational physics and particularly in the context of multiscale problems. The present paper offers a probabilis
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c12b85cd02f193c8aa6d3184de8a235b
Publikováno v:
PAMM. 17:865-868
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::75207e6b89224850dec5e2f1f88ede68
https://doi.org/10.1002/pamm.201710400
https://doi.org/10.1002/pamm.201710400
Publikováno v:
Journal of Computational Physics. 434:110218
The data-centric construction of inexpensive surrogates for fine-grained, physical models has been at the forefront of computational physics due to its significant utility in many-query tasks such as uncertainty quantification. Recent efforts have ta
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a5e8a81bedcf840a9b46e80cb6671ee0
https://doi.org/10.1016/j.jcp.2021.110218
https://doi.org/10.1016/j.jcp.2021.110218