The reliance on supercomputing facilities, and long processing times required for such high-performance simulations make them impractical for use in situations where real time information processing is required. Real time decision making, real time optimization and analysis under uncertainty are three of such situations. Indeed, accurate simulation of complex physical processes usually takes much longer than the actual physical time of the process. This is especially true for complex fluid-dynamics and general multi-physics processes. This makes the advanced CFD methods and some of the solutions for PDEs virtually useless in predicting the course of events or making real time decision and optimization of events which are currently in progress. A parallel of similar situation in the oil and gas industry is the smart field initiatives currently being entertained by many majors and national oil companies where real time decision making, real time optimization and real time analysis under uncertainty becomes a vital issue.
If simulators are to be used effectively for real time analysis, they must be capable of processing
potential scenarios much faster than their current speed. For mission critical processes such as real
time analysis of uncertainties associated with input parameters, real time decision making and real time
optimization where comparison of multiple scenarios becomes essential, simulators must approach real time
or near real time speeds and be capable of providing results in fraction of a second instead of minutes
or hours. The set of techniques that when combined is capable of such performance is based on hybrid
intelligent systems and include, but is not limited to, artificial neural networks,
genetic algorithms and fuzzy logic.
Using intelligent systems it is now possible to build Surrogate Intelligent Models
(SIMs) that can mimic functionalities of complex simulators in real time.
