An LLM acting as real-time controller for SIMP topology optimization parameters outperforms fixed schedules and heuristics, delivering 5.7-18.1% lower compliance on 2D and 3D benchmarks.
On the trajectories of penalization methods for topol- ogy optimization.Structural and Multidisciplinary Optimization, 21(2):128–139
2 Pith papers cite this work, alongside 146 external citations. Polarity classification is still indexing.
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Checkerboarding under SIMP with linear elements localizes to multiaxial load-transfer regions as a discrete stiff substitute for penalized continuous intermediate densities, while uniaxial regions remain free of the pattern.
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Large Language Models as Optimization Controllers: Adaptive Continuation for SIMP Topology Optimization
An LLM acting as real-time controller for SIMP topology optimization parameters outperforms fixed schedules and heuristics, delivering 5.7-18.1% lower compliance on 2D and 3D benchmarks.
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On the Localization of Checkerboarding in Multiaxial Stress Regions under SIMP Penalization
Checkerboarding under SIMP with linear elements localizes to multiaxial load-transfer regions as a discrete stiff substitute for penalized continuous intermediate densities, while uniaxial regions remain free of the pattern.