Zobrazeno 1 - 10
of 55
pro vyhledávání: '"Nils Thuerey"'
Publikováno v:
Data-Centric Engineering, Vol 4 (2023)
Modeling complex dynamical systems with only partial knowledge of their physical mechanisms is a crucial problem across all scientific and engineering disciplines. Purely data-driven approaches, which only make use of an artificial neural network and
Externí odkaz:
https://doaj.org/article/f82937f97a8d4cb080d70f9379313e72
Autor:
Stephan Rasp, Nils Thuerey
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 13, Iss 2, Pp n/a-n/a (2021)
Abstract Numerical weather prediction has traditionally been based on the models that discretize the dynamical and physical equations of the atmosphere. Recently, however, the rise of deep learning has created increased interest in purely data‐driv
Externí odkaz:
https://doaj.org/article/5f848c4969b54931aa850f880f59a1aa
Autor:
Stephan Rasp, Peter D. Dueben, Sebastian Scher, Jonathan A. Weyn, Soukayna Mouatadid, Nils Thuerey
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 12, Iss 11, Pp n/a-n/a (2020)
Abstract Data‐driven approaches, most prominently deep learning, have become powerful tools for prediction in many domains. A natural question to ask is whether data‐driven methods could also be used to predict global weather patterns days in adv
Externí odkaz:
https://doaj.org/article/db7463c2769d4534a00fa07937b96c7a
Publikováno v:
Proceedings of the Combustion Institute
This paper demonstrates the ability of neural networks to reliably learn the nonlinear flame response of a laminar premixed flame, while carrying out only one unsteady CFD simulation. The system is excited with a broadband, low-pass filtered velocity
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::33a8c5c0ea6236ced58b1d6bd38aeffa
https://doi.org/10.1016/j.proci.2020.07.115
https://doi.org/10.1016/j.proci.2020.07.115
Publikováno v:
Acta Astronautica. 175:11-18
Machine learning approach has been applied previously to physical problem such as complex fluid flows. This paper presents a method of using convolutional neural networks to directly predict the mixing characteristics between coolant film and combust
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e88ae51b8593151416f0dd2ee9cf4762
https://doi.org/10.1016/j.actaastro.2020.05.021
https://doi.org/10.1016/j.actaastro.2020.05.021
In this paper, we train turbulence models based on convolutional neural networks. These learned turbulence models improve under-resolved low resolution solutions to the incompressible Navier-Stokes equations at simulation time. Our study involves the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6fb50ee5779509a2ae6634cf87ae8389
Autor:
Benjamin J Holzschuh, Conor M O’Riordan, Simona Vegetti, Vicente Rodriguez-Gomez, Nils Thuerey
We examine the capability of generative models to produce realistic galaxy images. We show that mixing generated data with the original data improves the robustness in downstream machine learning tasks. We focus on three different data sets; analytic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5ebb9f37a1a316e634be6ddb987ee7da
Publikováno v:
CVPR
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
instname
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
instname
We propose a new generative model for 3D garment deformations that enables us to learn, for the first time, a data-driven method for virtual try-on that effectively addresses garment-body collisions. In contrast to existing methods that require an un
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::701a9b7ceac56e18f43a0cbe137a77dc
https://doi.org/10.1109/cvpr46437.2021.01159
https://doi.org/10.1109/cvpr46437.2021.01159
Publikováno v:
ACM Transactions on Applied Perception
ACM Transactions on Applied Perception, Association for Computing Machinery, 2021, 18 (2), pp.1-15. ⟨10.1145/3449064⟩
ACM Transactions on Applied Perception, Association for Computing Machinery, 2021, 18 (2), pp.1-15. ⟨10.1145/3449064⟩
Comparative evaluation lies at the heart of science, and determining the accuracy of a computational method is crucial for evaluating its potential as well as for guiding future efforts. However, metrics that are typically used have inherent shortcom
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::969daa99e8215a1e735dec62ae2d3df9
https://hal.telecom-paris.fr/hal-03225310
https://hal.telecom-paris.fr/hal-03225310
WeatherBench Probability: Medium-range weather forecasts with probabilistic machine learning methods
Because the atmosphere is inherently chaotic, probabilistic weather forecasts are crucial to provide reliable information. In this work, we present an extension to the WeatherBench, a benchmark dataset for medium-range, data-driven weather prediction
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::78942fc03d1c530210d1e3b6c93d56bc
https://doi.org/10.5194/egusphere-egu21-11448
https://doi.org/10.5194/egusphere-egu21-11448