This paper proposes a novel Bayesian strategy for high‐dimensional inverse problems in the context of elastostatics. Apart from parametric uncertainties, model inadequacies and, particularly, constitutive model errors, are also addressed. This is especially important in biomedical settings when the inferred material properties will be used to make decisions/diagnoses. Traditional approaches use an additional regression model (e.g., Gaussian process), added to the model output or within a submodel to account, for an underlying model error. This can violate physical constraints and becomes impractical in high dimensions.
Proceedings in Applied Mathematics & Mechanics – Wiley
Published: Jan 1, 2017
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