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Therefore, it is realistic that impulsive differential equations model such processes.
In Wood and Ackland's model such processes could also be at work [ 15].
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When modeling such processes, it is necessary to use random variables in jump conditions and impulsive differential equations with random impulses occurring at random moments.
This system in a simplified form models such processes as interaction between membranes and colloids, oscillations of transmission lines under action of rain and wind, and dynamics of suspension bridges and stay cables.
Although land surface models form an integral part of GCMs and topographic and hydrologic factors play an important role in local transmission models, such processes are more readily included within microsimulations that typically lack analytic transparency.
We present a sharp interface method for modeling such process.
There is no reason, in principle, that we can't model such causal processes in ABMs in order to help understand patterns of cultural change.
28 Models incorporating such processes can use computational simulation or inference methods to compare the model to the observed data, to fit parameter values and to compare alternative models in terms of their relative likelihoods.
In order to examine the epigenetics of differentiation, we must consider biological processes over time; the modeling of such processes may involve methods from time series analysis, Markov chain modeling, and dynamic Bayesian networks.
As detailed models of such processes turn out to be very complicated, POD is an attractive way to obtain reduced models of low order.
In order to be helpful, models for such processes and materials have to be provided as soon as possible even if the complexity of models for alternative materials lags behind contemporary models for silicon.
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