Computer simulations are widely used in research, yet many variables remain uncertain or could change during experiments. These unknowns create uncertainty, which can bring many challenges to researchers during experiments.
Enhancing the accuracy of computer experiments requires new statistical and data science methodologies that can help determine how these experiments should be designed, how data from the experiment should be analyzed and create more accurate simulations.
The research aims to establish a new uncertainty quantification (UQ) method, which is a method that seeks to minimize uncertainties in computational experiments, through experimental design, data analysis, and model validation and calibration.
Due to the variety and high use of simulations in research, creating and enhancing computer simulations through the development of new models to measure their efficiency and cost will help improve simulations and impact many areas of research.
Tuo will collaborate with Dr. Jeff Wu, co-principal investigator and professor at the Georgia Institute of Technology. Funding from the grant will also be used to support doctorate students who will develop new theories, and implement and compare methods.
Collaboration between UQ researchers and data science researchers will help improve statistical models, which will in turn improve simulations and the experiments that use this technology.
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