Zobrazeno 1 - 10
of 49
pro vyhledávání: '"Benjamin Peherstorfer"'
Publikováno v:
2022 American Control Conference (ACC).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::96e0891fa635e13196cbb6142a0fd31d
https://doi.org/10.23919/acc53348.2022.9867770
https://doi.org/10.23919/acc53348.2022.9867770
Autor:
Ionuț-Gabriel Farcaș, Benjamin Peherstorfer, Tobias Neckel, Frank Jenko, Hans-Joachim Bungartz
Publikováno v:
Computer Methods in Applied Mechanics and Engineering
Multi-fidelity Monte Carlo methods leverage low-fidelity and surrogate models for variance reduction to make tractable uncertainty quantification even when numerically simulating the physical systems of interest with high-fidelity models is computati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8b8b734f492f592e54e0842e60c91029
https://doi.org/10.1016/j.cma.2023.115908
https://doi.org/10.1016/j.cma.2023.115908
Autor:
Benjamin Peherstorfer
Publikováno v:
SIAM Journal on Scientific Computing. 42:A2803-A2836
This work presents a model reduction approach for problems with coherent structures that propagate over time such as convection-dominated flows and wave-type phenomena. Traditional model reduction methods have difficulties with these transport-domina
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dc1a2cf9d7c5f340fff820709209777b
https://doi.org/10.1137/19m1257275
https://doi.org/10.1137/19m1257275
Learning controllers from data for stabilizing dynamical systems typically follows a two step process of first identifying a model and then constructing a controller based on the identified model. However, learning models means identifying generic de
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c225a504d6831367981ef254ee80e330
http://arxiv.org/abs/2203.00474
http://arxiv.org/abs/2203.00474
Autor:
Julia Konrad, Ionuţ-Gabriel Farcaş, Benjamin Peherstorfer, Alessandro Di Siena, Frank Jenko, Tobias Neckel, Hans-Joachim Bungartz
Publikováno v:
Journal of Computational Physics
The linear micro-instabilities driving turbulent transport in magnetized fusion plasmas (as well as the respective nonlinear saturation mechanisms) are known to be sensitive with respect to various physical parameters characterizing the background pl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d0d48c9233c4dc86c6e075c04284bde1
https://hdl.handle.net/21.11116/0000-0009-EF8A-F21.11116/0000-0009-EF88-1
https://hdl.handle.net/21.11116/0000-0009-EF8A-F21.11116/0000-0009-EF88-1
Autor:
Zlatko Drmač, Benjamin Peherstorfer
Publikováno v:
Realization and Model Reduction of Dynamical Systems ISBN: 9783030951566
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4de4c4d74ea63881f184198d17415c78
https://doi.org/10.1007/978-3-030-95157-3_3
https://doi.org/10.1007/978-3-030-95157-3_3
This work introduces a data-driven control approach for stabilizing high-dimensional dynamical systems from scarce data. The proposed context-aware controller inference approach is based on the observation that controllers need to act locally only on
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e7e4518f5daf18f67d437db699fafedf
Publikováno v:
Journal of Scientific Computing. 88
This work introduces a non-intrusive model reduction approach for learning reduced models from partially observed state trajectories of high-dimensional dynamical systems. The proposed approach compensates for the loss of information due to the parti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::caf3ecb073e8d0cac822bf8d038c4bb0
https://doi.org/10.1007/s10915-021-01580-2
https://doi.org/10.1007/s10915-021-01580-2
Autor:
Benjamin Peherstorfer
Publikováno v:
SIAM/ASA Journal on Uncertainty Quantification. 7:579-603
Multifidelity Monte Carlo (MFMC) estimation combines low- and high-fidelity models to speed up the estimation of statistics of the high-fidelity model outputs. MFMC optimally samples the low- and h...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::64ad39fd79d524d70f79ebcd7fdf2315
https://doi.org/10.1137/17m1159208
https://doi.org/10.1137/17m1159208
Operator inference learns low-dimensional dynamical-system models with polynomial nonlinear terms from trajectories of high-dimensional physical systems (non-intrusive model reduction). This work focuses on the large class of physical systems that ca
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b24497da3323d3d161c7aeac31e8476a