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contributor authorOrorbia, Maximilian E.
contributor authorWarn, Gordon P.
date accessioned2024-12-24T19:14:04Z
date available2024-12-24T19:14:04Z
date copyright6/3/2024 12:00:00 AM
date issued2024
identifier issn1050-0472
identifier othermd_146_9_091707.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4303551
description abstractStructural design synthesis considering discrete elements can be formulated as a sequential decision process solved using deep reinforcement learning, as shown in prior work. By modeling structural design synthesis as a Markov decision process (MDP), the states correspond to specific structural designs, the discrete actions correspond to specific design alterations, and the rewards are related to the improvement in the altered design’s performance with respect to the design objective and specified constraints. Here, the MDP action definition is extended by integrating parametric design grammars that further enable the design agent to not only alter a given structural design’s topology, but also its element parameters. In considering topological and parametric actions, both the dimensionality of the state and action space and the diversity of the action types available to the agent in each state significantly increase, making the overall MDP learning task more challenging. Hence, this paper also addresses discrete design synthesis problems with large state and action spaces by significantly extending the network architecture. Specifically, a hierarchical-inspired deep neural network architecture is developed to allow the agent to learn the type of action, topological or parametric, to apply, thus reducing the complexity of possible action choices in a given state. This extended framework is applied to the design synthesis of planar structures considering both discrete elements and cross-sectional areas, and it is observed to adeptly learn policies that synthesize high performing design solutions.
publisherThe American Society of Mechanical Engineers (ASME)
titleDiscrete Structural Design Synthesis: A Hierarchical-Inspired Deep Reinforcement Learning Approach Considering Topological and Parametric Actions
typeJournal Paper
journal volume146
journal issue9
journal titleJournal of Mechanical Design
identifier doi10.1115/1.4065488
journal fristpage91707-1
journal lastpage91707-13
page13
treeJournal of Mechanical Design:;2024:;volume( 146 ):;issue: 009
contenttypeFulltext


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