A Statistical Framework for the Design and Analysis of Multi-Fidelity Computer Experiments

University Of Wisconsin-Madison, Madison WI

Investigators

Abstract

The research objective of this award is to develop a new statistical framework for the design, sampling and modeling of such simulations. Multi-fidelity computer modeling is emerging as a popular method for studying complex systems in engineering. This method combines a large number of approximate simulations with a smaller number of detailed simulations for building prediction models, calibration and optimization. The research will result in tools and techniques which are general enough to apply to a large array of engineering problems in which two computer models are available and one model is more accurate but more expensive than the other. The research effort is focused on the development of a new sampling scheme for estimating the expected values of the outputs of a set of multi-fidelity computer simulations, a new type of statistical design for efficiently running multi-fidelity computer simulations and novel statistical methods for modeling multi-fidelity computer simulations with qualitative and quantitative factors. Challenging real-world problems from the industry and national labs will be used to test and validate the developed results. If successful, the results of this research will provide engineers a statistics-guided framework for efficiently conducting multi-fidelity computer simulations. Example applications include conceptual design, electronic cooling, hydrology, impact dynamics, material design, nanotechnology, oil reserve management, polymer electrolyte fuel cells manufacturing, thermal dynamics and vehicle multi-body dynamics. Computer simulations are now widely used for solving several pressing issues faced by the U.S. and the world such as climate change, energy conservation and renewable/clean energy innovation. The developed results will potentially enable researchers in these critical fields to use simulations to tackle problems of much larger scales. The research will be disseminated as open source software to directly benefit users of multi-fidelity computer experiments and make a long-term impact. Graduate and undergraduate statistics and engineering students will benefit through involvement in the research and new course offering.