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Introduction
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The demand for ever-increasing computational power and bandwidth is met by multidisciplinary science, such as co-simulation of the heart, brain and circulatory system. The most significant bottleneck in cardiac simulation, however, is not the simulator [@saad_2012], but the cardiovascular model. More complex models result in slower simulations, such as those using the Richards formalism [@richards_1985]. The efficiency of these simulations can be improved by decreasing the number of [@lin_2006]. The decision for using one of these is based on factors such as [@lin_2006]:
– how well the model can be fitted to experimental data
– the compatibility of the model with other established models
– whether there is a need for a thorough simulation study.
If a low-fidelity model is sufficient for what the cardiac physiologists need to simulate, it is worth considering if the model will fit their needs and if they have a need to ask for more accurate simulations.
The quality of the cardiovascular model is not an issue in low-fidelity co-simulation. Furthermore, there is no need for a thorough investigation of the cardiovascular system; sometimes just an overview is enough to reach the desired conclusions. For co-simulation, however, one must consider how the user wants to go about the simulation and what is to be simulated [@chen_2015]. In co-simulation, the basis of each model must be compatible with the model intended for co-simulation [@chen_2015]. The compatibility of these models is only meaningful in the sense that all the needed data can be exchanged and all the required boundary conditions can be specified [@chen_2015]. In that case, the cardiovascular model in this paper is compatible with the existing low-fidelity models.
A way to improve the quality of low-fidelity models is to parameterize them using a well-established model [@lin_2006]. The parameterization does not have to be perfect for the parameterized model to be usable in co-simulation; it must just be sufficient for the simulation of the physiologists’ needs [@lin_2006].
This work investigates whether using different model-parameterization methods is sufficient to create a framework for co-simulation.
The objective of this study was to assess whether increasing the parameterization quality of the
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