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    Discrete Structural Design Synthesis: A Hierarchical-Inspired Deep Reinforcement Learning Approach Considering Topological and Parametric Actions 

    Source: Journal of Mechanical Design:;2024:;volume( 146 ):;issue: 009:;page 91707-1
    Author(s): Ororbia, Maximilian E.; Warn, Gordon P.
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Structural 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 ...
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    Quantifying the Shape of Pareto Fronts During Multi-Objective Trade Space Exploration 

    Source: Journal of Mechanical Design:;2018:;volume( 140 ):;issue: 002:;page 21402
    Author(s): Unal, Mehmet; Warn, Gordon P.; Simpson, Timothy W.
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Recent advances in simulation and computation capabilities have enabled designers to model increasingly complex engineering problems, taking into account many dimensions, or objectives, in the problem formulation. Increasing ...
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    Probabilistic Assessment of the Life-Cycle Environmental Performance and Functional Life of Buildings due to Seismic Events 

    Source: Journal of Architectural Engineering:;2018:;Volume ( 024 ):;issue: 001
    Author(s): Chhabra Jaskanwal P. S.;Hasik Vaclav;Bilec Melissa M.;Warn Gordon P.
    Publisher: American Society of Civil Engineers
    Abstract: Quantifying the environmental impacts of buildings is a key step in realizing resilient and sustainable buildings. Buildings are vulnerable to natural hazards during their functional life, and the structural performance ...
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