• Jim Kiles

Vayu Theory: Addressing Uncertainty with Science and Computationally Prescribed Interruptions

Vayu executes evolving computational algorithms* on complex data*at every possible point in time based on all relevant data from earlier points in time and the known circumstances and data of those earlier times (that can be determined) then calculates for interruptions that will cause the preferred result.

In the circumstance of uncertain, changing conditions, each point in time brings its representation of the state of affairs at that moment and relates to an overall, evolving state of affairs (which is constantly changing) and is different at every point in time. Each point in time exists in its identified state because of what has happened at every previous point in time AND will impact the state of affairs at every future point in time.

Vayu is based on the simple premise of identifying as many states of affairs as possible at as many known, specific points in time as can be acquired, then applying existing/accepted scientific theory (as represented in calculus and algorithms representing that calculus) to all data at known times to develop a predicted state of affairs at future points in time.

Vayu looks at every possible current state of affairs and identifies whether a preferred future dataset exists and, if so what interruption in the scientifically predicted future state of affairs would cause the preferred result and bring about a more favorable state of affairs.

Vayu then simulates the success of the proposed interruption at future moments in time and identifies the changed state of affairs for the forecast time period and runs through its process again.

High performance computing allows Vayu to run millions of calculations on all known datasets to bring about preferred future datasets on which the Vayu process begins again.

Key to the Vayu process is the existence of advanced scientific theory, calculus and algorithms about the specific set of uncertain conditions, an understanding of preferred results and a set of possible interruptions that may bring about the known preferred results.

Vayu then measures performance of its process to comprehend the extent of the success of its application to the datasets and calculates for the accuracy of the uncertain conditions science, calculus and algorithms on a continuous basis. This enables the final step and key to the Vayu process, the refinement of the formulas for more accuracy in achieving the preferred results.


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