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Estimating a patient‐specific computational model's parameters relies on data that is often unreliable and ill‐suited for a deterministic approach. We develop an optimization‐based uncertainty quantification framework for probabilistic model tuning that discovers model inputs distributions that generate target output distributions. Probabilistic sampling is performed using a surrogate model for computational efficiency, and a general distribution parameterization is used to describe each input. The approach is tested on seven patient‐specific modeling examples using CircAdapt, a cardiovascular circulatory model. Six examples are synthetic, aiming to match the output distributions generated using known reference input data distributions, while the seventh example uses real‐world patient data for the output distributions. Our results demonstrate the accurate reproduction of the target output distributions, with a correct recreation of the reference inputs for the six synthetic examples. Our proposed approach is suitable for determining the parameter distributions of patient‐specific models with uncertain data and can be used to gain insights into the sensitivity of the model parameters to the measured data.

Charge transport in molecular solids, such as semiconducting polymers, is strongly affected by packing and structural order over several length scales. Conventional approaches to modeling these phenomena range from analytical models to numerical models using quantum mechanical calculations. While analytical approaches cannot account for detailed structural effects, numerical models are expensive for exhaustive (and statistically significant) analysis. Here, we report a computationally scalable methodology using graph theory to explore the influence of molecular ordering on charge mobility. This model accurately reproduces the analytical results for transport in nematic and isotropic systems, as well as experimental results of the dependence of the charge carrier mobility on orientation correlation length for polymers. We further model how defect distribution (correlated and uncorrelated) in semiconducting polymers can modify the mobility, predicting a critical defect density above which the mobility plummets. This work enables rapid (and computationally extensible) evaluation of charge mobility semiconducting polymer devices.