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New research on multi-information sources of multiphysics systems was published Oct. 25 online in the Journal of Aerospace Information Systems. Sequential Information-Theoretic and Reification-Based Approach for Querying Multi-Information Sources was written by Seyede Fatemeh Ghoreishi, a postdoctoral researcher working with ISR-affiliated Assistant Professor Mark Fuge (ME) and Assistant Professor Axel Krieger (ME); Dillon Tomison of Lockheed Martin; and Douglas Alliare, an Assistant Professor in the Department of Mechanical Engineering at Texas A&M University.

The work was supported by the Air Force Office of Scientific Research Multi-Disciplinary University Research Initiative on multi-information sources of multiphysics systems under award number FA9550-15-1-0038, and by the National Science Foundation under grant number CMMI-1663130.

About the research
While the growing number of computational models available to designers can solve a lot of problems, it complicates the process of properly using the information provided by each simulator. It may seem intuitive to select the model with the highest accuracy, or fidelity, as decision makers want the greatest degree of certainty to increase their efficacy. However, high-fidelity models often come at a high computational expense. While comparatively lacking in veracity, low-fidelity models do contain some degree of useful information that can be obtained at a low cost.

The paper proposes a sequential method to use this information to generate a fused model with predictive capability superior to any of its constituent models. The researchers’ methodology estimates the correlation between each model using a model reification approach that eliminates the observational data requirement. The correlation is then used in an updating procedure whereby uncertain outputs from multiple models may be fused together to better estimate some quantity or quantities of interest. These ingredients are used in a decision-theoretic manner to query from multiple information sources sequentially to achieve the maximum knowledge about the fused model in as few information source evaluations as possible with minimum cost. This approach has the potential to significantly improve information fusion from multifidelity information sources.



October 30, 2019


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