Assessing parameter identifiability of a hemodynamics PDE model using spectral surrogates and dimension reduction.

ABSTRACT: Computational inverse problems for biomedical simulators suffer from limited data and relatively high parameter dimensionality. This often requires sensitivity analysis, where parameters of the model are ranked based on their influence on the specific quantities of interest. This is especially important for simulators used to build medical digital twins, as the amount of data is typically limited. For expensive models, such as blood flow models, emulation is employed to expedite the simulation time. Parameter ranking and fixing using sensitivity analysis are often heuristic, though, and vary with the specific application or simulator used. The present study provides an innovative solution to this problem by leveraging polynomial chaos expansions (PCEs) for both multioutput global sensitivi

SUBMITTER: Colebank MJ 

PROVIDER: S-EPMC12520355 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature