23,293 research outputs found
Finding weakly reversible realizations of chemical reaction networks using optimization
An algorithm is given in this paper for the computation of dynamically
equivalent weakly reversible realizations with the maximal number of reactions,
for chemical reaction networks (CRNs) with mass action kinetics. The original
problem statement can be traced back at least 30 years ago. The algorithm uses
standard linear and mixed integer linear programming, and it is based on
elementary graph theory and important former results on the dense realizations
of CRNs. The proposed method is also capable of determining if no dynamically
equivalent weakly reversible structure exists for a given reaction network with
a previously fixed complex set.Comment: 18 pages, 9 figure
Finding complex balanced and detailed balanced realizations of chemical reaction networks
Reversibility, weak reversibility and deficiency, detailed and complex
balancing are generally not "encoded" in the kinetic differential equations but
they are realization properties that may imply local or even global asymptotic
stability of the underlying reaction kinetic system when further conditions are
also fulfilled. In this paper, efficient numerical procedures are given for
finding complex balanced or detailed balanced realizations of mass action type
chemical reaction networks or kinetic dynamical systems in the framework of
linear programming. The procedures are illustrated on numerical examples.Comment: submitted to J. Math. Che
Dynamic mode decomposition with control
We develop a new method which extends Dynamic Mode Decomposition (DMD) to
incorporate the effect of control to extract low-order models from
high-dimensional, complex systems. DMD finds spatial-temporal coherent modes,
connects local-linear analysis to nonlinear operator theory, and provides an
equation-free architecture which is compatible with compressive sensing. In
actuated systems, DMD is incapable of producing an input-output model;
moreover, the dynamics and the modes will be corrupted by external forcing. Our
new method, Dynamic Mode Decomposition with control (DMDc), capitalizes on all
of the advantages of DMD and provides the additional innovation of being able
to disambiguate between the underlying dynamics and the effects of actuation,
resulting in accurate input-output models. The method is data-driven in that it
does not require knowledge of the underlying governing equations, only
snapshots of state and actuation data from historical, experimental, or
black-box simulations. We demonstrate the method on high-dimensional dynamical
systems, including a model with relevance to the analysis of infectious disease
data with mass vaccination (actuation).Comment: 10 pages, 4 figure
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