Bayesian Physics Informed Neural Networks for real-world nonlinear dynamical systems
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Erschienen in
-
In: Computer Methods in Applied Mechanics and Engineering 420: 115346 (2022-12-01)
- Ereignis
-
Veröffentlichung
- (wo)
-
Hamburg
- (wer)
-
Universitätsbibliothek der Technischen Universität Hamburg
- (wann)
-
2022
- Urheber
- DOI
-
10.15480/882.4767
- URN
-
urn:nbn:de:gbv:830-882.0201399
- Rechteinformation
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
15.08.2025, 07:38 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Linka, Kevin
- Schäfer, Amelie
- Meng, Xuhui
- Zou, Zongren
- Karniadakis, George
- Kuhl, Ellen
- Universitätsbibliothek der Technischen Universität Hamburg
Entstanden
- 2022
Ähnliche Objekte (12)
Solving real-world optimization tasks using physics-informed neural computing
Informed Bayesian survival analysis
A physics-informed Bayesian optimization method for rapid development of electrical machines
Bayesian Physics-informed Neural Networks for system identification of inverter-dominated power systems
A framework for Li-ion battery prognosis based on hybrid Bayesian physics-informed neural networks
A physics-informed Bayesian framework for characterizing ground motion process in the presence of missing data
A physics informed bayesian optimization approach for material design: application to NiTi shape memory alloys
Time series analysis informed by dynamical systems theory
Stiff-PDEs and Physics-Informed Neural Networks
Physics-informed shape optimization using coordinate projection
Physics informed neural networks for continuum micromechanics
Physics‐Informed Inverse Design of Programmable Metasurfaces
Solving real-world optimization tasks using physics-informed neural computing
Informed Bayesian survival analysis
A physics-informed Bayesian optimization method for rapid development of electrical machines
Bayesian Physics-informed Neural Networks for system identification of inverter-dominated power systems
A framework for Li-ion battery prognosis based on hybrid Bayesian physics-informed neural networks
A physics-informed Bayesian framework for characterizing ground motion process in the presence of missing data
A physics informed bayesian optimization approach for material design: application to NiTi shape memory alloys
Time series analysis informed by dynamical systems theory
Stiff-PDEs and Physics-Informed Neural Networks
Physics-informed shape optimization using coordinate projection
Physics informed neural networks for continuum micromechanics
Physics‐Informed Inverse Design of Programmable Metasurfaces
Solving real-world optimization tasks using physics-informed neural computing
Informed Bayesian survival analysis
A physics-informed Bayesian optimization method for rapid development of electrical machines
Bayesian Physics-informed Neural Networks for system identification of inverter-dominated power systems
A framework for Li-ion battery prognosis based on hybrid Bayesian physics-informed neural networks
A physics-informed Bayesian framework for characterizing ground motion process in the presence of missing data
A physics informed bayesian optimization approach for material design: application to NiTi shape memory alloys
Time series analysis informed by dynamical systems theory
Stiff-PDEs and Physics-Informed Neural Networks
Physics-informed shape optimization using coordinate projection
Physics informed neural networks for continuum micromechanics