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Soft tissue models must capture complex, multiphase, and time-dependent behavior. However, the high computational cost of such analyses often restricts them to simplified deterministic scenarios that neglect biological variability and uncertainty. In this paper, the deterministic foundation of a liver perfusion-function model based on the Finite Element Method (FEM) is extended to incorporate material variability. To this end, a workflow is presented that integrates sensitivity analysis, surrogate modeling based on a Gradient-Enhanced Kriging (GEK) model, and uncertainty quantification. This approach enables the identification of critical parameters, enhances the interpretability of the model, and provides probabilistic outcome assessments for FEM-based simulations. The FEM model represents liver tissue on the lobule scale, incorporating metabolically active cells and vascular systems through a poroelastic multiscale framework with spatially coupled function-perfusion dynamics. It simulates liver-related conditions, such as tumor growth due to metabolic associated fatty liver disease (MAFLD), which affects microperfusion. Aleatoric uncertainties in material parameters are accounted for, and a local sensitivity analysis identifies critical nodes as a foundation for a GEK metamodel. Monte Carlo (MC) methods on the surrogate enable fast propagation of input uncertainty and probabilistic assessment of predicted outcomes. Rather than assuming deterministic accuracy, this approach offers a systematic way to explore and interpret model behavior under uncertainty.<\/jats:p>","DOI":"10.1007\/s10237-026-02084-4","type":"journal-article","created":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T06:33:20Z","timestamp":1785393200000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Uncertainty quantification in FEM simulation of human liver: sensitivity analysis, Gradient-Enhanced Kriging, and Monte Carlo simulation"],"prefix":"10.1007","volume":"25","author":[{"given":"Navina","family":"Waschinsky","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carmen","family":"van Meegen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lena","family":"Lambers","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katja","family":"Ickstadt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tim","family":"Ricken","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,30]]},"reference":[{"key":"2084_CR1","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1007\/s10237-010-0205-y","volume":"9","author":"GA Ateshian","year":"2010","unstructured":"Ateshian GA, Ricken T (2010) Multigenerational interstitial growth of biological tissues. 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