  Computational cardiac modelling is a mature area of biomedical computing, and
is currently evolving from a pure research tool to aiding in clinical decision
making. Assessing the reliability of computational model predictions is a key
factor for clinical use, and uncertainty quantification (UQ) and sensitivity
analysis are important parts of such an assessment. In this study, we apply new
methods for UQ in computational heart mechanics to study uncertainty both in
material parameters characterizing global myocardial stiffness and in the local
muscle fiber orientation that governs tissue anisotropy. The uncertainty
analysis is performed using the polynomial chaos expansion (PCE) method, which
is a non-intrusive meta-modeling technique that surrogates the original
computational model with a series of orthonormal polynomials over the random
input parameter space. In addition, in order to study variability in the muscle
fiber architecture, we model the uncertainty in orientation of the fiber field
as an approximated random field using a truncated Karhunen-Lo\'eve expansion.
The results from the UQ and sensitivity analysis identify clear differences in
the impact of various material parameters on global output quantities.
Furthermore, our analysis of random field variations in the fiber architecture
demonstrate a substantial impact of fiber angle variations on the selected
outputs, highlighting the need for accurate assignment of fiber orientation in
computational heart mechanics models.
