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Predictive modelling aims to combine knowledge (understanding and data) with the goal of predicting system dynamics; conversely, exploratory models focus on developing understanding in systems where uncertainty is high.
ANN has been viewed as a useful tool for managers in predicting system behaviors.
Also, the sensors' data are useful for predicting system operation and network failures.
These characteristics make frequency response methods particularly suitable to characterizing and predicting system response to transients.
However, existing reliability modelling approaches lack any methodologies to take account of maintenance actions in predicting system failure probability.
The model was tested to evaluate accuracy in predicting system behavior at different process conditions and pollutant concentrations.
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iCFD tools could play a crucial role in reliably predicting systems' performance under normal and shock events.
A computational model can reveal, in the same way as an animal model, key cellular behaviors by performing virtual perturbations and predicting system-wide outcomes.
The utilities of such modeling include predicting systems behavior, identifying crucial steps in systems regulation.
As mentioned above the potential of outcome predicting systems concerning the judgment about an individual therapy may be limited.
Those collective properties often called "emergent properties"—are critical attributes of biological systems, as understanding the individual parts alone is insufficient to understand or predict system behaviour.
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