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Found 6 papers, 1 papers with code
Coarse-graining Complex Networks for Control Equivalence
The ability to control complex networks is of crucial importance across a wide range of applications in natural and engineering sciences.
Optimality-preserving Reduction of Chemical Reaction Networks
In many applications, for example, in systems biology and epidemiology, CRN parameters such as the kinetic reaction rates can be used as control inputs to steer the system toward a given target.
Exact maximal reduction of stochastic reaction networks by species lumping
Motivation: Stochastic reaction networks are a widespread model to describe biological systems where the presence of noise is relevant, such as in cell regulatory processes.
Improved estimations of stochastic chemical kinetics by finite state expansion
The deterministic rate equation (DRE) gives a macroscopic approximation as a compact system of differential equations that estimate the average populations for each species, but it may be inaccurate in the case of nonlinear interaction dynamics.
CLUE: Exact maximal reduction of kinetic models by constrained lumping of differential equations
Model reduction can help tame such complexity by providing a lower-dimensional model in which each macro-variable can be directly related to the original variables.
Learning Queuing Networks by Recurrent Neural Networks
It is well known that building analytical performance models in practice is difficult because it requires a considerable degree of proficiency in the underlying mathematics.
