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Empowering Human Decision-Making in AI Models: The Path to Trust and Transparency
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.85, No.4, pp. 299-328, 2012, DOI:10.3970/cmes.2012.085.299
Abstract This paper presents a meshless Galerkin level-set method (MGLSM) for shape and topology optimization of compliant mechanisms of geometrically nonlinear structures. The design boundary of the mechanism is implicitly described as the zero level set of a Lipschitz continuous level set function of higher dimension. The moving least square (MLS) approximation is used to construct the meshless shape functions with the global Galerkin weak-form in terms of a set of arbitrarily distributed nodes. The MLS shape function is first employed to parameterize the level set function via the surface fitting rather than interpolation, and then used to implement the meshless… More >
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