The phrase "a very large model" is correct and usable in written English.
You can use it when describing the size or scale of a model, often in contexts like data science, machine learning, or physical models.
Example: "The researchers developed a very large model to analyze the complex data set and improve prediction accuracy."
Alternatives: "an enormous model" or "a massive model".
Of course this is too seldom in the case of fadvance calls with a very large model, and too often in the case of for i=1,100000 x+=i.
However, a very large model will take unnecessarily long computing time.
Computationally less demanding than the grid search method, the GA inversion is capable of efficiently search a very large model space, where the model parameters can be freely defined with a few assumptions and restrictions.
Distributed hybrid control systems involving extremely large number of interacting control loops, coordinating large number of autonomous agents, handling very large model uncertainties will be in the center of future research.
Specifically, there is a need to store, query, and transform very large models in an efficient way.
The analysis of the dynamics of very large models is however a rather hard problem, which led us to previously introduce the Process Hitting framework (PH), which is a particular class of nondeterministic asynchronous automata network (or safe Petri nets).
Reasoning about sets of states rather than individual states, and using a partition reduction allows us to scale-up to very large models.
This shows that we are close to a functional understanding of the HOG, PKC and MAT pathways; that this functional understanding can be expressed within the rxncon formalism; and that the iterative model building and bipartite Boolean simulation is a potent tool to identify and correct missing or erroneous features in even very large models.
Multiresolution enhancements make it practical to simulate and store very large models.
Most involve the use of highly explosive liquid sodium, which requires very large models.
Parametrisation and repetition make it easy to design very large models of regular structure.
