|
Lumping |
Conservation Analysis |
Time scale separation |
Sensitivity
analysis |
Balanced Truncation |
Artificial neural network |
Empirical
approximation |
System agnosticism |
Utility depends on the nature of the full-order
model to be reduced. Not all models are amenable to these methods |
Utility depends on the nature of the full-order model to be reduced. Not
all models are amenable to these methods |
Utility depends on the nature
of the full-order model to be reduced. Not all models are amenable to
these methods |
Utility depends on the nature of the full-order model to
be reduced. Not all models are amenable to these methods |
Have no
requirement about the nature of the full-order model |
Have no
requirement about the nature of the full-order model |
Have no
requirement about the nature of the full-order model |
Automatability |
Yes |
Yes |
Some variants are automatable |
Yes |
Yes |
Yes |
No |
Mechanistic relevance |
Yes |
Yes |
Yes |
Yes |
No |
No |
No |
Accuracy |
Determined by the user as a trade-off of complexity vs
accuracy |
No loss of accuracy incurred- but only minimal model-order
reduction is possible with this technique |
Variable, depending on the
system |
Variable, depending on the system |
Determined by the user as a
trade-off of complexity vs accuracy |
Accuracy can be controlled by the
size of the network |
Can be any level of accuracy required but only for
1 or 3 response variables |
Computational ease |
Computationally demanding for large nonlinear
models |
Efficient |
Variable |
Computationally demanding for large
nonlinear models |
|
Efficient, although upfront cost of simulating
pseudo-data may be high in some situations |
Very efficient, limited
data needed |
Predictive ability of the reduced model (interpolation and/or
extrapolation) |
Both |
Retains all properties of the full-order model
including extrapolation ability |
Both |
Both |
Possibly, Can be used to
describe data based on the input and output functions used to generate
the reduced model |
Can only predict within the range of pseudo-data
(interpolation). Not useful for extrapolation |
Can only predict within
the range of pseudo-data (interpolation). Not useful for
extrapolation |
Requirement of experimental data for reduced model parameter estimation |
No |
No |
No |
No |
No |
Yes – needs data or pseudo-data to create
model structure |
Yes – needs data or pseudo-data to create model
structure |
Suitable for parameter estimation |
Yes |
Yes |
Yes |
Yes |
No |
No |
Yes |