Data models as a general fast framework for converting simulations at all scales into fast real-time approximations
Holger M. Jaenisch, James Handley · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2012
Data Modeling is a process that can convert non-real-time algorithms into functional approximations that can be executed in near real-time as platform independent mathematical equations or information transfer functions. These functional approximations are converted into a form amenable for streaming real-time execution by being converted into pre-calculated look-up table (LUT) form. We present the technique and relevant theory and demonstrate how this method can be applied to high level interactions, system level modeling and component modeling using a common framework. An important benefit of our technique is the ability to predict anomalous parameters from our models.