Should You Derive or Let the Data Drive? Towards a Hybrid Physics-based Data-driven Symbiosis
Monday, February 22, 2016 - 3:55pm - 4:10pm
Mathematical models are employed ubiquitously for description, prediction and decision making. In addressing end-goal objectives, great care needs to be devoted to attainment of appropriate balance of inexactness throughout the various stages of the end goal process (e.g. modeling and optimization). Disregard to such considerations, either entails redundant computation or impairment of the overall fidelity of the optimization process. Model reduction is instrumental in trading-off fidelity for computation, yet, in some situations, it is essential to take an opposite viewpoint, and enhance model fidelity and thereby end-goal output. In this talk, we shall describe how such framework has been utilized for real-life application in the natural resources sector.