Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Fabian Franzelin"'
Autor:
Christian Rohde, Markus Köppel, Dirk Pflüger, Fabian Franzelin, Ilja Kröker, Wolfgang Nowak, Andrea Barth, Bernard Haasdonk, Gabriele Santin, Dominik Wittwar, Sergey Oladyshkin
Publikováno v:
Computational Geosciences. 23:339-354
A variety of methods is available to quantify uncertainties arising within the modeling of flow and transport in carbon dioxide storage, but there is a lack of thorough comparisons. Usually, raw data from such storage sites can hardly be described by
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::39cb89cb20e8ac3424a01c52bde6ad9d
https://doi.org/10.1007/s10596-018-9785-x
https://doi.org/10.1007/s10596-018-9785-x
Polynomial chaos expansions (PCE) are well-suited to quantifying uncertainty in models parameterized by independent random variables. The assumption of independence leads to simple strategies for evaluating PCE coefficients. In contrast, the applicat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::65ae595f5e436673c64e18fd18acbb15
http://arxiv.org/abs/1903.09682
http://arxiv.org/abs/1903.09682
Publikováno v:
Reliability Engineering & System Safety. 209:107430
Robust prediction of the behavior of complex physical and engineering systems relies on approximating solutions in terms of physical and stochastic domains. For higher resolution and accuracy, simulation models must increase the number of determinist
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c672428f90f99dca79db5e9d5425d725
https://doi.org/10.1016/j.ress.2021.107430
https://doi.org/10.1016/j.ress.2021.107430
Publikováno v:
International Journal of Fracture. 201:157-170
This paper shows a new approach to estimate the critical traction for Mode I crack opening before crack growth by numerical simulation. For quasi-static loading, Linear Elastic Fracture Mechanics predicts the critical traction before crack growth. To
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::384abe533dc547a30f7722e8542feae0
https://doi.org/10.1007/s10704-016-0119-5
https://doi.org/10.1007/s10704-016-0119-5
Autor:
Fabian Franzelin, Dirk Pflüger
Publikováno v:
Lecture Notes in Computational Science and Engineering ISBN: 9783319754253
Sparse grid interpolants of high-dimensional functions do not maintain the range of function values. This is a core problem when one is dealing with probability density functions, for example. We present a novel approach to limit range of function va
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1a7b342ac3f4f92d9422c7938474b116
https://doi.org/10.1007/978-3-319-75426-0_4
https://doi.org/10.1007/978-3-319-75426-0_4
Autor:
Dirk Pflüger, Fabian Franzelin
Publikováno v:
Lecture Notes in Computational Science and Engineering ISBN: 9783319282602
We present a novel data-driven approach to propagate uncertainty. It consists of a highly efficient integrated adaptive sparse grid approach. We remove the gap between the subjective assumptions of the input’s uncertainty and the unknown real distr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bc9b6cbec1f39926d3b059740474d2d1
https://doi.org/10.1007/978-3-319-28262-6_2
https://doi.org/10.1007/978-3-319-28262-6_2
Publikováno v:
Lecture Notes in Computational Science and Engineering ISBN: 9783319045368
We present a novel method to tackle the multi-class classification problem with sparse grids and show how the computational procedure can be split into an Offline phase (pre-processing) and a very rapid Online phase. For each class of the training da
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dcb6208f1867a5481d4ed5a000e24d3d
https://doi.org/10.1007/978-3-319-04537-5_11
https://doi.org/10.1007/978-3-319-04537-5_11