A probabilistic ROM framework calibrates correction factors for a generalized one-fiber model using Bayesian inference on full-order isogeometric cardiac data and uses Gaussian processes for online prediction with uncertainty quantification.
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The paper reviews recent developments and unresolved challenges in cardiac mechanics modeling, arguing that identifying essential complexities versus safe simplifications is key to clinical translation.
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A probabilistic reduced-order modeling framework for patient-specific cardio-mechanical analysis
A probabilistic ROM framework calibrates correction factors for a generalized one-fiber model using Bayesian inference on full-order isogeometric cardiac data and uses Gaussian processes for online prediction with uncertainty quantification.
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Cardiac mechanics modeling: recent developments and current challenges
The paper reviews recent developments and unresolved challenges in cardiac mechanics modeling, arguing that identifying essential complexities versus safe simplifications is key to clinical translation.