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Learning between digital twins

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FHTW01 - Uncertainty quantification for cardiac models

This work is motivated by, and is part of, a project that aim to develop digital twins for essential hypertension management and treatment through physically based computer models,  new sensor data and traditional population based data. Our approach is that the individual digital twins should learn from each other. We explore doing this by combining Bayesian model calibration and mixed models for simplified models. This is work in progress.

This talk is part of the Isaac Newton Institute Seminar Series series.

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