Large engineered systems and infrastructure, such as buildings and airplanes, pose a challenge to the interdisciplinary teams who work together to design them. Today's architects, engineers and designers use computational tools to help generate ideas and simulate and analyze specific aspects of the behaviors of large systems. However, the larger and more complex those systems become, the more it becomes necessary to integrate all aspects of design into a unified design tool. This Leading Engineering for America's Prosperity, Health, and Infrastructure (LEAP-HI) award supports fundamental research on a hybrid approach that enables computers and humans to collaborate in system-level design through the use of emerging computational techniques from machine learning, convex optimization, image processing and artificial intelligence. The work has the potential to benefit US industrial innovation and competitiveness by enhancing design creativity and exploration, thereby improving product performance and safety. The project integrates expertise in the disciplines of product design, architectural and structural systems, and mechanical engineering and includes activities to broaden the participation of underrepresented groups and improve engineering and architecture education.

A new, machine-learning-based model for creative design space exploration that enables architects, designers, and engineers to collaborate creatively and effectively will be researched. The method links design decisions to real-time performance predictions without the need for rigidly pre-specifying decision parameters, combining the freedom of analog design methods with the power of computation. For large, complex, engineered systems, the research effort will focus on leveraging artificial intelligence and machine learning approaches to assist system architects in extracting a designer?s intent and formulating that intent as a set of hierarchically structured functional requirements that can be maintained as subsystems of systems. Deep learning tools and algorithms will be used to represent and embed the design functions that underlie the methods. Studies of the use of computational tools for convex engineering by engineering teams will be conducted to understand how hybrid intelligent approaches to design impact notions of design intent in complex system design.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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Massachusetts Institute of Technology
United States
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