The researchers will 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.
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. Computational Foundations for Learning, Verifying, and Applying Model Simplification Rules is a three-year, $265K award.