Prepared by: Ali Farhang Mehr Group Lead: Irem Tumer Complex Systems Design Group Title: Planning and Design of Experiments for Measurement of Physical Parameters Application Context: The proposed approach is directly applicable when the distribution of a physical parameter should be approximated or modeled. For example: Example 1: To find the temperature distribution of upper troposphere/lower stratosphere (that directly affects flight missions). Example 2: To approximate the concentration and density distribution in the Atlantic Ocean (that can help model the Thermohaline Circulation). etc. Objective: The objective of the proposed research is to identify the 'best' points, in terms of location and number, where measurements must be made so that 'the most accurate' approximation model can be obtained for a physical phenomenon. Approach: An information-theoretic approach is used to quantify the expected 'worth of information' obtained by conducting a particular measurement (i.e. experiment). An interim Bayesian approximation model is then built that can be used to estimate parameter distribution based on the information at hand. This can be used, in a sequential manner, to plan the next experiments such that 'maximum information is obtained about the unknown distribution of the physical parameter with minimum number of experiments'. Our Related Research Articles: "Bayesian Meta-Modeling of Engineering Design Simulations: A Sequential Approach with Adaptation to Irregularities in the Response Behavior", International Journal for Numerical Methods in Engineering Vol. 32(4), pp. 491-514 "Meta-Modeling of Multi-Response Engineering Simulations", 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Albany, NY