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So, prediction using a single history matched model involves more risk because of the parameter uncertainty.
Of course, if a model involves more unknown functions, it is difficult to obtain the asymptotic behavior of traveling wave solutions by the inequalities similar to (3.6) and (3.7).
The flipped classroom model involves more than shifting content delivery outside of class time [ 6].
The exon-increase model involves more in the variation of existed exons/introns, e.g., their length, divergence and GC content, but less in their gain and loss.
These results were considered significant since bacterial survival in a rat agar bead model involves more that just intra-macrophage survival.
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However, in our experience, fitting becomes difficult for any model involving more than two or three parameters.
A more comprehensive model involving more complex genetic events may be required (Sibley et al. 2001; Hastings et al. 2002a, b; Hyde 2002).
We refer to [2, 8, 9] for details on physical models involving more general reaction-diffusion equations.
The results show that the curved beam element models, involving more degrees of freedom and coupling of translational and torsional motion, are more accurate at simulating the static and dynamic characters of an iced quad-conductor bundle.
Many models involve more than one unknown.
Parameter identification is an important issue in computational biology since most of the models involve more parameters than the available data.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com