Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Kast, Mariella"'
We investigate reduced-order models for acoustic and electromagnetic wave problems in parametrically defined domains. The parameter-to-solution maps are approximated following the so-called Galerkin POD-NN method, which combines the construction of a
Externí odkaz:
http://arxiv.org/abs/2406.13567
Autor:
Kast, Mariella, Hesthaven, Jan S
This work extends the paradigm of evolutional deep neural networks (EDNNs) to solving parametric time-dependent partial differential equations (PDEs) on domains with geometric structure. By introducing positional embeddings based on eigenfunctions of
Externí odkaz:
http://arxiv.org/abs/2308.03461
The dynamic behavior of jointed assemblies exhibiting friction nonlinearities features amplitude-dependent dissipation and stiffness. To develop numerical simulations for predictive and design purposes, macro-scale High Fidelity Models (HFMs) of the
Externí odkaz:
http://arxiv.org/abs/2204.12160
Autor:
Kast, Mariella, Hesthaven, Jan S.
Publikováno v:
In Journal of Computational Physics 1 July 2024 508
Publikováno v:
In Mechanical Systems and Signal Processing 15 February 2023 185
Publikováno v:
In Computer Methods in Applied Mechanics and Engineering 1 June 2020 364
We propose a non-intrusive reduced basis (RB) method for parametrized nonlinear partial differential equations (PDEs) that leverages models of different accuracy. The method extracts parameter locations from a collection of low-fidelity (LF) snapshot
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::371335a19e37f892fdf08b698d5c1967
We propose a non-intrusive reduced basis (RB) method for parametrized nonlinear partial differential equations (PDEs) that leverages models of different accuracy. The method extracts parameter locations from a collection of low-fidelity (LF) snapshot
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______185::a4b812bd1b3a3b4a384d91f9ae83c811
https://infoscience.epfl.ch/record/265226
https://infoscience.epfl.ch/record/265226