Uncertainty quantification of elastic material responses: testing, stochastic calibration and Bayesian model selection
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
2524-5619
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
online resource.
- Erschienen in
-
Uncertainty quantification of elastic material responses: testing, stochastic calibration and Bayesian model selection ; volume:1 ; number:1 ; day:23 ; month:11 ; year:2019 ; pages:1-18 ; date:12.2019
Mechanics of soft materials ; 1, Heft 1 (23.11.2019), 1-18, 12.2019
- Urheber
-
Fitt, Danielle
Wyatt, Hayley
Woolley, Thomas E.
Mihai, L. Angela
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
-
10.1007/s42558-019-0013-1
- URN
-
urn:nbn:de:101:1-2020090520531320976581
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
14.08.2025, 10:51 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
- Fitt, Danielle
- Wyatt, Hayley
- Woolley, Thomas E.
- Mihai, L. Angela
- SpringerLink (Online service)
Ähnliche Objekte (12)
Bayesian calibration, process modeling and uncertainty quantification in biotechnology
Comparing frequentist and Bayesian uncertainty quantification in two‐step constitutive model calibration
Bayesian sensor calibration
Improved uncertainty quantification for neural networks with Bayesian last layer
Inverse Uncertainty Quantification in Material Parameter Calibration Using Probabilistic and Interval Approaches
Quantification of nisin concentration from fluorescence‐based antimicrobial activity assay using Bayesian calibration
Bayesian calibration of coupled computational mechanics models under uncertainty based on interface deformation
Forward uncertainty quantification with special emphasis on a Bayesian active learning perspective
Bayesian uncertainty quantification in aerosol optical depth retrieval applied to TROPOMI measurements
Advanced parameter estimation, Bayesian uncertainty quantification, and surrogate modeling for chromatography processes
Multifaceted uncertainty quantification
Uncertainty quantification in multivariable regression for material property prediction with Bayesian neural networks
Bayesian calibration, process modeling and uncertainty quantification in biotechnology
Comparing frequentist and Bayesian uncertainty quantification in two‐step constitutive model calibration
Bayesian sensor calibration
Improved uncertainty quantification for neural networks with Bayesian last layer
Inverse Uncertainty Quantification in Material Parameter Calibration Using Probabilistic and Interval Approaches
Quantification of nisin concentration from fluorescence‐based antimicrobial activity assay using Bayesian calibration
Bayesian calibration of coupled computational mechanics models under uncertainty based on interface deformation
Forward uncertainty quantification with special emphasis on a Bayesian active learning perspective
Bayesian uncertainty quantification in aerosol optical depth retrieval applied to TROPOMI measurements
Advanced parameter estimation, Bayesian uncertainty quantification, and surrogate modeling for chromatography processes
Multifaceted uncertainty quantification
Uncertainty quantification in multivariable regression for material property prediction with Bayesian neural networks
Bayesian calibration, process modeling and uncertainty quantification in biotechnology
Comparing frequentist and Bayesian uncertainty quantification in two‐step constitutive model calibration
Bayesian sensor calibration
Improved uncertainty quantification for neural networks with Bayesian last layer
Inverse Uncertainty Quantification in Material Parameter Calibration Using Probabilistic and Interval Approaches
Quantification of nisin concentration from fluorescence‐based antimicrobial activity assay using Bayesian calibration
Bayesian calibration of coupled computational mechanics models under uncertainty based on interface deformation
Forward uncertainty quantification with special emphasis on a Bayesian active learning perspective
Bayesian uncertainty quantification in aerosol optical depth retrieval applied to TROPOMI measurements
Advanced parameter estimation, Bayesian uncertainty quantification, and surrogate modeling for chromatography processes
Multifaceted uncertainty quantification