关注
Philipp A. Guth
Philipp A. Guth
在 ricam.oeaw.ac.at 的电子邮件经过验证
标题
引用次数
引用次数
年份
A quasi-Monte Carlo method for optimal control under uncertainty
PA Guth, V Kaarnioja, FY Kuo, C Schillings, IH Sloan
SIAM/ASA Journal on Uncertainty Quantification 9 (2), 354-383, 2021
372021
14 Ensemble Kalman filter for neural network-based one-shot inversion
PA Guth, C Schillings, S Weissmann
Optimization and Control for Partial Differential Equations: Uncertainty …, 2022
182022
Parabolic PDE-constrained optimal control under uncertainty with entropic risk measure using quasi-Monte Carlo integration
PA Guth, V Kaarnioja, FY Kuo, C Schillings, IH Sloan
Numerische Mathematik 156 (2), 565-608, 2024
152024
A quasi-Monte Carlo method for an optimal control problem under uncertainty
PA Guth, V Kaarnioja, FY Kuo, C Schillings, IH Sloan
arXiv preprint arXiv:1910.10022, 2019
102019
Generalized dimension truncation error analysis for high-dimensional numerical integration: lognormal setting and beyond
PA Guth, V Kaarnioja
SIAM Journal on Numerical Analysis 62 (2), 872-892, 2024
42024
Multilevel quasi-Monte Carlo for optimization under uncertainty
PA Guth, A Van Barel
Numerische Mathematik 154 (3-4), 443-484, 2023
42023
Ensemble Feedback Stabilization of Linear Systems
PA Guth, K Kunisch, SS Rodrigues
arXiv preprint arXiv:2306.01079, 2023
22023
One-shot Learning of Surrogates in PDE-constrained Optimization Under Uncertainty
PA Guth, C Schillings, S Weissmann
arXiv preprint arXiv:2112.11126, 2021
22021
Tracking optimal feedback control under uncertain parameters
PA Guth, K Kunisch, SS Rodrigues
Physica D: Nonlinear Phenomena, 134245, 2024
2024
Application of dimension truncation error analysis to high-dimensional function approximation To appear in: 2022. Springer Verlag, 2024.
PA Guth, V Kaarnioja
Springer Verlag, 2024
2024
Stabilization of uncertain linear dynamics: an offline-online strategy
PA Guth, K Kunisch, SS Rodrigues
arXiv preprint arXiv:2307.14090, 2023
2023
Application of dimension truncation error analysis to high-dimensional function approximation in uncertainty quantification
PA Guth, V Kaarnioja
arXiv preprint arXiv:2301.13693, 2023
2023
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