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This new algorithm bears strong resemblance with the Dual-EnKF, but unlike the latter which first propagates the state with the model then updates it with the new observation, the proposed scheme starts by an update step, followed by a model integration step.
Figure 2 illustrates this model integration step with an example.
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The model integration time step was 27 s, and monthly mean values of all hydrodynamics and biogeochemical variables were stored throughout the entire simulation time span.
Our method originates in nonlinear system identification theory, and is predicated on representing uncertain variables as pseudo-random multi-level signals (PRMSs), which are imposed on the system model during each time integration step of a dynamic optimization.
We believe that our approach can serve as a preprocessing step for model integration and the rule-based validation methodology can be applied to other ontology formats.
This work follows a recognizable gap characterized by high-level steps towards model integration: the Science-to-Information Technology barrier.
We have added the second integration step to this model (now Figure 10) and expanded the legend to explain this in more detail.
For that purpose, the ODE model is altered by adding Gaussian noise at every integration step to the previous concentration (building a SDE, Stochastic Differential Equation model).
Subsequently, as a late integration step, the continuous outputs of these models were added.
Step (2) is derived from the optimal model integration (Huang and Hsu, 2002).
Only our model has two nearly pure integration steps: the tVOR integrator is a pure integrator and the velocity-to-position neural integrator has the usual time constant of 20 seconds, and thus this model best maintains eye velocity during sustained translation.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com