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RISReinforcement Learning for Efficient Design Space Exploration With Variable Fidelity Analysis Models
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Reinforcement learning algorithms can autonomously learn to search a design space for highperformance solutions. However, modern engineering often entails the use of computationally intensive simulation, which can lead to ...
Adaptive Learning of Design Policies Over Nonhierarchical Multi-Fidelity Models Guided by Policy Alignment
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Abstract. Multifidelity reinforcement learning (RL) frameworks significantly enhance the efficiency of engineering design by leveraging analysis models with varying levels of accuracy and computational costs. The prevailing ...
Expanding the Generative Power of Large Language Models for Design Through Formal Design Grammars and Languages
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Abstract. Research in design grammars has been underway for over 50 years and has demonstrated great generative power for a wide range of design and engineering domains. A key limitation, though, is the lack of support for ...