Penn State developed methods to use artificial intelligence to explore design spaces for complex systems. The methods used game engines to model physics, and a variety of AI architectures (recurrent neural networks, generative adversarial networks) to learn the rules for generating satisfactory designs. The AI learned how to generate both physical configurations and behaviors. The methods were generated on a variety of examples, to include air vehicles, rotorcraft, soaring aircraft, and sailing vessels. Designs were analyzed computationally, and also fabricated and tested via scale models.
Generative Adversarial Networks for Design Exploration and Refinement (GANDER)
2019
59 pages
Report
No indication
English
Computers, Control & Information Theory , Neural networks , Artificial intelligence , Aircrafts , Artificial intelligence computing , Artificial intelligence software , Contracts , Pennsylvania , Universities , Complex systems , Information operations , National guard , Scale models , Classification , Contract administration , Rotary wing aircraft , Standards , Vehicles , Governments , Machine learning , Models
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