Considering discrepancy when calibrating a mechanistic electrophysiology model

@article{Lei2020ConsideringDW,
  title={Considering discrepancy when calibrating a mechanistic electrophysiology model},
  author={Chon Lok Lei and Sanmitra Ghosh and Dominic G. Whittaker and Yasser Aboelkassem and Kylie A. Beattie and Chris D. Cantwell and Tammo Delhaas and Charles Houston and Gustavo Montes Novaes and Alexander V. Panfilov and Pras Pathmanathan and Marina Riabiz and Rodrigo Weber dos Santos and John Walmsley and Keith Worden and Gary R. Mirams and Richard D. Wilkinson},
  journal={Philosophical transactions. Series A, Mathematical, physical, and engineering sciences},
  year={2020},
  volume={378}
}
Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations to take decisions. The field of cardiac simulation has begun to explore and adopt UQ methods to characterize uncertainty in model inputs and how that propagates through to outputs or predictions; examples of this can be seen in the papers of this issue. In this review and perspective piece, we draw attention to an important and under-addressed source of uncertainty in our predictions—that of uncertainty… 

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An audit of uncertainty in multi-scale cardiac electrophysiology models

The sources of uncertainty in these models at different spatial scales are reviewed, how uncertainties are communicated across scales are discussed, and how their relative importance is assessed are discussed.

Calibration of ionic and cellular cardiac electrophysiology models

A review of the classic and latest approaches to calibration in the electrophysiology field, at both the ion channel and cellular AP scales, and the need for reproducible descriptions of the calibration process to enable models to be recalibrated to new data sets and built upon for new studies.

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