Collaborative Research: Computational Foundations for Learning, Verifying, and Applying Model Simplification Rules

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The objective of this award is to develop feature-based simplification of computer-aided-design models, specifically to accelerate and automate downstream finite-element-analysis. In particular, the research will create algorithmic foundations for learning conservative feature suppression rules from demonstrations performed by human experts. The effect of simplification on simulation accuracy will be formally characterized and this understanding will be used to create robust algorithms for feature suppression within computer-aided design models. Research findings will be integrated into graduate and undergraduate curriculum. The research will ultimately lead to a framework to automatically learn, validate, and apply context dependent model simplification rules that can be audited by human experts, and deployed to automate the model simplification task.If successful, the research will significantly speed up model simplification, and enhance the automated use of engineering analysis tools in the design process. Potential applications include design of heat exchangers, aircraft structures, and semi-conductor equipment. The planned research and education integration activities and outreach activities will familiarize graduate, undergraduate, and high school students with the use of model simplification technologies in challenging engineering design projects. This project will also increase awareness among practicing engineers about the potential usage of automated model simplification in complex engineering design projects. Students working on this project will also participate in Badger Camp and Engineering Expo events at the University of Wisconsin campus and Maryland Day at the University of Maryland to enhance the public understanding of the science and technology.