Generative Adversarial Networks for Design Exploration and Refinement (GANDER)

Abstract

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.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Sep 30, 2019
Accession Number
AD1121088

Entities

People

  • Michael Yukish

Organizations

  • Pennsylvania State University

Tags

Communities of Interest

  • Advanced Electronics
  • Air Platforms

DTIC Thesaurus Topics

  • Aircrafts
  • Artificial Intelligence
  • Artificial Intelligence Computing
  • Artificial Intelligence Software
  • Assembly
  • Classification
  • Complex Systems
  • Computational Science
  • Computers
  • Contract Administration
  • Contracts
  • Control Systems
  • Databases
  • Energy Harvesting
  • Engineering
  • Failure Mode And Effect Analysis
  • High Performance Computing
  • Information Operations
  • Information Science
  • Language
  • Lessons Learned
  • Machine Learning
  • Manufacturing
  • Models
  • National Guard
  • Neural Networks
  • Organizational Structure
  • Pennsylvania
  • Point Clouds
  • Recurrent Neural Networks
  • Rotary Wing Aircraft
  • Scale Models
  • Universities
  • Wind Turbines

Fields of Study

  • Computer science

Readers

  • Aviation Science / Aeronautics.
  • Computational Modeling and Simulation
  • Geospatial Intelligence and Artificial Intelligence Analytics

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks
  • Space