Hochschulschrift
Physics-informed neural networks for learning dynamic, distributed and uncertain systems
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
Stuttgart, Universität Stuttgart, Dissertation, 2023
- Bibliographic citation
-
Mitteilungen / Institut für Wasser- und Umweltsystemmodellierung, Universität Stuttgart ; 300
- Keyword
-
Neuronales Netz
Unsicherheit
Nichtlineares dynamisches System
Systemidentifikation
Mathematisches Modell
Modell
- Event
-
Veröffentlichung
- (where)
-
Stuttgart
- (who)
-
Universitätsbibliothek der Universität Stuttgart
- (when)
-
2023
- Creator
- Contributor
- DOI
-
10.18419/opus-13229
- URN
-
urn:nbn:de:bsz:93-opus-ds-132481
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
25.03.2025, 1:55 PM CET
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Object type
- Hochschulschrift
Associated
- Praditia, Timothy
- Nowak, Wolfgang
- Universitätsbibliothek der Universität Stuttgart
Time of origin
- 2023
Other Objects (12)
Physics-informed neural networks for learning dynamic, distributed and uncertain systems
Physics-informed neural networks applied to catastrophic creeping landslides
Learning free-surface flow with physics-informed neural networks
Parareal with a physics-informed neural network as coarse propagator
Bayesian Physics Informed Neural Networks for real-world nonlinear dynamical systems
Shape‐optimization of extrusion‐dies via parameterized physics‐informed neural networks
Simulation and Optimization of Gas Transport Problems using Physics-Informed Neural Networks
Effects of variational formulations on physics‐informed neural network performance in solid mechanics
Bayesian Physics-informed Neural Networks for system identification of inverter-dominated power systems
Hybrid Modeling of Lithium-Ion Battery: Physics-Informed Neural Network for Battery State Estimation
Quantitative Hemodynamics using Magnetic Resonance Imaging, Computational Fluid Dynamics and Physics-informed Neural Network
Application and extension of a super resolution physics-informed convolutional neural network to groundwater modelling
Physics-informed neural networks for learning dynamic, distributed and uncertain systems
Physics-informed neural networks applied to catastrophic creeping landslides
Learning free-surface flow with physics-informed neural networks
Parareal with a physics-informed neural network as coarse propagator
Bayesian Physics Informed Neural Networks for real-world nonlinear dynamical systems
Shape‐optimization of extrusion‐dies via parameterized physics‐informed neural networks
Simulation and Optimization of Gas Transport Problems using Physics-Informed Neural Networks
Effects of variational formulations on physics‐informed neural network performance in solid mechanics
Bayesian Physics-informed Neural Networks for system identification of inverter-dominated power systems
Hybrid Modeling of Lithium-Ion Battery: Physics-Informed Neural Network for Battery State Estimation
Quantitative Hemodynamics using Magnetic Resonance Imaging, Computational Fluid Dynamics and Physics-informed Neural Network
Application and extension of a super resolution physics-informed convolutional neural network to groundwater modelling
Physics-informed neural networks for learning dynamic, distributed and uncertain systems
Physics-informed neural networks applied to catastrophic creeping landslides
Learning free-surface flow with physics-informed neural networks
Parareal with a physics-informed neural network as coarse propagator
Bayesian Physics Informed Neural Networks for real-world nonlinear dynamical systems
Shape‐optimization of extrusion‐dies via parameterized physics‐informed neural networks
Simulation and Optimization of Gas Transport Problems using Physics-Informed Neural Networks
Effects of variational formulations on physics‐informed neural network performance in solid mechanics
Bayesian Physics-informed Neural Networks for system identification of inverter-dominated power systems
Hybrid Modeling of Lithium-Ion Battery: Physics-Informed Neural Network for Battery State Estimation
Quantitative Hemodynamics using Magnetic Resonance Imaging, Computational Fluid Dynamics and Physics-informed Neural Network