DeepAI AI Chat
Log In Sign Up

Considering discrepancy when calibrating a mechanistic electrophysiology model

by   Chon Lok Lei, et al.

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 characterise uncertainty in model inputs and how that propagates through to outputs or predictions. In this perspective piece we draw attention to an important and under-addressed source of uncertainty in our predictions — that of uncertainty in the model structure or the equations themselves. The difference between imperfect models and reality is termed model discrepancy, and we are often uncertain as to the size and consequences of this discrepancy. Here we provide two examples of the consequences of discrepancy when calibrating models at the ion channel and action potential scales. Furthermore, we attempt to account for this discrepancy when calibrating and validating an ion channel model using different methods, based on modelling the discrepancy using Gaussian processes (GPs) and autoregressive-moving-average (ARMA) models, then highlight the advantages and shortcomings of each approach. Finally, suggestions and lines of enquiry for future work are provided.


page 14

page 34

page 38


Bayesian Calibration of imperfect computer models using Physics-informed priors

In this work we introduce a computational efficient data-driven framewor...

Learning on dynamic statistical manifolds

Hyperbolic balance laws with uncertain (random) parameters and inputs ar...

Deep Learning to advance the Eigenspace Perturbation Method for Turbulence Model Uncertainty Quantification

The Reynolds Averaged Navier Stokes (RANS) models are the most common fo...

Embedded model discrepancy: A case study of Zika modeling

Mathematical models of epidemiological systems enable investigation of a...

Anticipatory Ethics and the Role of Uncertainty

Making conjectures about future consequences of a technology is an exerc...