Hochschulschrift
Multi-fidelity Gaussian process surrogate models for numerical acoustic design in frequency domain
- Weitere Titel
-
Multi-fidelity Gaussian process surrogates in numerical acoustics
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISBN
-
9783844097528
384409752X
- Maße
-
21 cm, 235 g
- Umfang
-
xi, 160 Seiten
- Sprache
-
Englisch
- Anmerkungen
-
Illustrationen
Technische Universität Hamburg, Dissertation, 2024
- Erschienen in
-
Schriftenreihe des Instituts für Modellierung und Berechnung der Technischen Universität Hamburg ; Band 39
- Reihe
-
Schriftenreihe des Instituts für Modellierung und Berechnung der Technischen Universität Hamburg; Band 39
- Schlagwort
-
Technische Akustik
Numerisches Modell
Gauß-Prozess
Finite-Elemente-Methode
Finite-Elemente-Methode
Numerisches Modell
- Ereignis
-
Veröffentlichung
- (wo)
-
Düren
- (wer)
-
Shaker Verlag
- (wann)
-
2025
- Urheber
- Beteiligte Personen und Organisationen
- Inhaltsverzeichnis
- Rechteinformation
-
Bei diesem Objekt liegt nur das Inhaltsverzeichnis digital vor. Der Zugriff darauf ist unbeschränkt möglich.
- Letzte Aktualisierung
- 17.02.2026, 12:06 UTC
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.
Objekttyp
- Hochschulschrift
Beteiligte
- Kohlsche, Thomas (Verfasser)
- Shaker Verlag (Verlag)
- Shaker Verlag
Entstanden
- 2025
Ähnliche Objekte (12)
Multi-fidelity Surrogate Modelling of Wall Mounted Cubes
Multi‐fidelity Gaussian processes for an efficient approximation of frequency sweeps in acoustic problems
Aerodynamic optimization of turbine airfoils using multi-fidelity surrogate models
Multi-fidelity surrogate-based optimal design of road vehicle suspension systems
Multi-fidelity User Interface Specifications
Gaussian process based surrogate modelling of acoustic systems
Gaussian process based surrogate modelling of acoustic systems
Constrained multi-fidelity surrogate framework using Bayesian optimization with non-intrusive reduced-order basis
Downscaling precipitation over High Mountain Asia using Multi-Fidelity Gaussian Processes: Improved estimates from ERA5
Graph Laplacian-based spectral multi-fidelity modeling
Multi‐Fidelity Optimization using Reduced Order Models
Multi-fidelity Modeling and Optimization of Biogas Plants
Multi-fidelity Surrogate Modelling of Wall Mounted Cubes
Multi‐fidelity Gaussian processes for an efficient approximation of frequency sweeps in acoustic problems
Aerodynamic optimization of turbine airfoils using multi-fidelity surrogate models
Multi-fidelity surrogate-based optimal design of road vehicle suspension systems
Multi-fidelity User Interface Specifications
Gaussian process based surrogate modelling of acoustic systems
Gaussian process based surrogate modelling of acoustic systems
Constrained multi-fidelity surrogate framework using Bayesian optimization with non-intrusive reduced-order basis
Downscaling precipitation over High Mountain Asia using Multi-Fidelity Gaussian Processes: Improved estimates from ERA5
Graph Laplacian-based spectral multi-fidelity modeling
Multi‐Fidelity Optimization using Reduced Order Models
Multi-fidelity Modeling and Optimization of Biogas Plants
Multi-fidelity Surrogate Modelling of Wall Mounted Cubes
Multi‐fidelity Gaussian processes for an efficient approximation of frequency sweeps in acoustic problems
Aerodynamic optimization of turbine airfoils using multi-fidelity surrogate models
Multi-fidelity surrogate-based optimal design of road vehicle suspension systems
Multi-fidelity User Interface Specifications
Gaussian process based surrogate modelling of acoustic systems
Gaussian process based surrogate modelling of acoustic systems
Constrained multi-fidelity surrogate framework using Bayesian optimization with non-intrusive reduced-order basis
Downscaling precipitation over High Mountain Asia using Multi-Fidelity Gaussian Processes: Improved estimates from ERA5
Graph Laplacian-based spectral multi-fidelity modeling
Multi‐Fidelity Optimization using Reduced Order Models