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Project: Dynamic model extraction
The idea behind dynamic model extraction is to use a hybrid
approach between statistical regression modeling and detailed
mathematical process modeling to determine a system of coupled
differential equations whose trajectories span the state space of the
data. The objective is to obtain a physically or physiologically
meaningful model without the need for detailed process modeling. To use dynamic model extraction a canonical mathematical
representation is chosen for the system of differential equations and
model parameters are estimated using optimization in the state
space over the vector field generated by a data set from a
dynamic process. The approach requires longitudinal data sampled over
time and does not require that the sampling intervals be equal. Dynamic model
extraction methods may be used to determine dynamic
models that can produce trajectories consistent with the dynamics of physical
or biological processes, even for conditions outside the range of
available data. Examples of dynamic model extraction can be found by
following the links below.
Douglasfir forest stand
development Metabolic pathway analysis
