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- W2939117798 abstract "The work presented in this thesis deals with the development of a Bayesian calibration framework for parameters of nonlinear computational models of arterial growth. By the incorporation of measurements from longitudinal image data in combination with a novel dimensionality reduction approach, this work demonstrates the feasibility of the predictive use of large-scale, patient-specific computational models for arterial growth. The probabilistic formulation provides a basis for the prospective application of such models in the clinical management routine." @default.
- W2939117798 created "2019-04-25" @default.
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- W2939117798 date "2017-01-01" @default.
- W2939117798 modified "2023-09-27" @default.
- W2939117798 title "Bayesian Calibration of Nonlinear Cardiovascular Models for the Predictive Simulation of Arterial Growth" @default.
- W2939117798 hasPublicationYear "2017" @default.
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