Operator-informed machine learning: Extracting geometry and dynamics from time series data
- Alternative title
-
Operatorenbasiertes machinelles Lernen: Extraktion von Geometrie und Dynamik aus Zeitreihendaten
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
München, Technische Universität München, Dissertation, 2022
- Keyword
-
Time
Dynamics
Geometry
Machine learning
- Event
-
Veröffentlichung
- (where)
-
München
- (who)
-
Universitätsbibliothek der TU München
- (when)
-
2022
- Creator
-
Lehmberg, Daniel
- Contributor
- URN
-
urn:nbn:de:bvb:91-diss-20220411-1637386-1-3
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
25.03.2025, 1:46 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.
Associated
- Lehmberg, Daniel
- Bungartz, Hans-Joachim
- Köster, Gerta
- Universitätsbibliothek der TU München
Time of origin
- 2022
Other Objects (12)
Extracting Hadamard Coefficients from Green's Operators
Extracting intelligent information from aerial images using machine learning
Dirac operators in Riemannian geometry
Horizontal Dirac Operators in CR Geometry
Physics-informed machine learning to model rapid complex geohazards
A Monge-Ampère Operator in Symplectic Geometry
Geometry of state spaces of operator algebras
Physics-Informed Quantum Machine Learning for Solving Partial Differential Equations
Extracting channel heads from digital elevation models (DEM) using machine learning techniques
Machine-learning techniques for geometry optimization
Optimizing System Reliability in Additive Manufacturing Using Physics-Informed Machine Learning
Informed Machine Learning: Integrating Prior Knowledge into Data-Driven Learning Systems
Extracting Hadamard Coefficients from Green's Operators
Extracting intelligent information from aerial images using machine learning
Dirac operators in Riemannian geometry
Horizontal Dirac Operators in CR Geometry
Physics-informed machine learning to model rapid complex geohazards
A Monge-Ampère Operator in Symplectic Geometry
Geometry of state spaces of operator algebras
Physics-Informed Quantum Machine Learning for Solving Partial Differential Equations
Extracting channel heads from digital elevation models (DEM) using machine learning techniques
Machine-learning techniques for geometry optimization
Optimizing System Reliability in Additive Manufacturing Using Physics-Informed Machine Learning
Informed Machine Learning: Integrating Prior Knowledge into Data-Driven Learning Systems
Extracting Hadamard Coefficients from Green's Operators
Extracting intelligent information from aerial images using machine learning
Dirac operators in Riemannian geometry
Horizontal Dirac Operators in CR Geometry
Physics-informed machine learning to model rapid complex geohazards
A Monge-Ampère Operator in Symplectic Geometry
Geometry of state spaces of operator algebras
Physics-Informed Quantum Machine Learning for Solving Partial Differential Equations
Extracting channel heads from digital elevation models (DEM) using machine learning techniques
Machine-learning techniques for geometry optimization
Optimizing System Reliability in Additive Manufacturing Using Physics-Informed Machine Learning