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Koopman Operators for Generalized Persistence of Excitation Conditions for Nonlinear Systems
[article]
2019
arXiv
pre-print
It is hard to identify nonlinear biological models strictly from data, with results that are often sensitive to experimental conditions. Automated experimental workflows and liquid handling enables unprecedented throughput, as well as the capacity to generate extremely large datasets. We seek to develop generalized identifiability conditions for informing the design of automated experiments to discover predictive nonlinear biological models. For linear systems, identifiability is characterized
arXiv:1906.10274v2
fatcat:qm3re76ahjerpiv3kkw6fq7i2y