Zobrazeno 1 - 10 of 68 pro vyhledávání: '"Alexander Heinlein"'
4
Combining Machine Learning and Adaptive Coarse Spaces---A Hybrid Approach for Robust FETI-DP Methods in Three Dimensions
Autor: Alexander Heinlein, Martin Lanser, Janine Weber, Axel Klawonn
Publikováno v: SIAM Journal on Scientific Computing. 43:S816-S838
The hybrid ML-FETI-DP algorithm combines the advantages of adaptive coarse spaces in domain decomposition methods and certain supervised machine learning techniques. Adaptive coarse spaces ensure robustness of highly scalable domain decomposition sol
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::5a77c41c4b11fc93fab8a30f49bf1b93
https://doi.org/10.1137/20m1344913
Zobrazit plný text záznamu
5
Estimating the time-dependent contact rate of SIR and SEIR models in mathematical epidemiology using physics-informed neural networks
Autor: Janine Weber, Alexander Heinlein, Viktor Grimm, Axel Klawonn, Martin Lanser
Publikováno v: ETNA - Electronic Transactions on Numerical Analysis. 56:1-27
The course of an epidemic can be often successfully described mathematically using compartment models. These models result in a system of ordinary differential equations. Two well-known examples are the SIR and the SEIR models. The transition rates
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::cabffe7e8a848285e55594d73368079d
https://doi.org/10.1553/etna_vol56s1
Zobrazit plný text záznamu
6
Coupling regional air quality simulations of EURAD-IM with street canyon observations - a machine learning approach
Autor: Charlotte Neubacher, Philipp Franke, Alexander Heinlein, Axel Klawonn, Astrid Kiendler-Scharr, Anne-Caroline Lange
State of the art atmospheric chemistry transport models on regional scales as the EURAD-IM (EURopean Air pollution Dispersion-Inverse Model) simulate physical and chemical processes in the atmosphere to predict the dispersion of air pollutants. With
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::c073544efe822fec58af4a2cf880e0df
https://doi.org/10.5194/egusphere-egu22-7113
Zobrazit plný text záznamu
7
A frugal FETI-DP and BDDC coarse space for heterogeneous problems
Autor: Axel Klawonn, Martin Lanser, Alexander Heinlein, Janine Weber
Publikováno v: ETNA - Electronic Transactions on Numerical Analysis. 53:562-591
The convergence rate of domain decomposition methods is generally determined by the eigenvalues of the preconditioned system. For second-order elliptic partial differential equations, coefficient discontinuities with a large contrast can lead to a de
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::3aff0f9d4d35047e5d41907a6c083bd2
https://doi.org/10.1553/etna_vol53s562
Zobrazit plný text záznamu
8
Additive and Hybrid Nonlinear Two-Level Schwarz Methods and Energy Minimizing Coarse Spaces for Unstructured Grids
Autor: Martin Lanser, Alexander Heinlein
Publikováno v: SIAM Journal on Scientific Computing. 42:A2461-A2488
Nonlinear domain decomposition (DD) methods, such as ASPIN (additive Schwarz preconditioned inexact Newton), RASPEN (restricted additive Schwarz preconditioned inexact Newton), nonlinear FETI-DP (f...
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ad82d1d5e8c8a6cb68d4db8cb6c75e7b
https://doi.org/10.1137/19m1276972
Zobrazit plný text záznamu
10
Surrogate convolutional neural network models for steady computational fluid dynamics simulations
Autor: Matthias Eichinger, Alexander Heinlein, Axel Klawonn
Publikováno v: Electronic Transactions on Numerical Analysis, 56
A convolution neural network (CNN)-based approach for the construction of reduced order surrogate models for computational fluid dynamics (CFD) simulations is introduced; it is inspired by the approach of Guo, Li, and Iori [X. Guo, W. Li, and F. Iori
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b90834e648ef7df2ef001ddb0b78ec7f
https://doi.org/10.1553/etna_vol56s235
Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání

Omezení vyhledávání
Plný text Recenzováno Digitální knihovna AV ČR
Zdroje