eCNNTO applies an element-wise CNN with residual connections and final-stage training data to accelerate density-based topology optimization while generalizing across boundary conditions, loads, geometries, and mesh sizes.
Numerical instabilities in topology optimization: a survey on procedures dealing with checkerboards, mesh-dependencies and local minima
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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.
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.
IterSIMP-σ integrates multimodal LLMs for proposing spatial density interventions in stress-aware SIMP topology optimization, yielding comparable but statistically non-significant performance gains over rule-based baselines on 2D and 3D benchmarks.
An isogeometric topology optimization approach using topological derivatives and level-set methods in an immersed framework enables seamless geometry updates without remeshing and benefits from higher-order basis functions for solution accuracy.
A sequential topology optimization approach uses SIMP results to initialize level-set refinement via signed distance function transfer on 3D meshes, achieving comparable compliance with up to 4.6x speedup on benchmarks.
citing papers explorer
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eCNNTO: A Highly Generalizable ConvNet for Accelerating Topology Optimization
eCNNTO applies an element-wise CNN with residual connections and final-stage training data to accelerate density-based topology optimization while generalizing across boundary conditions, loads, geometries, and mesh sizes.
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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.
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IterSIMP-{\sigma}: Evaluating LLM-Assisted Spatial Interventions in Stress-Aware Topology Optimization
IterSIMP-σ integrates multimodal LLMs for proposing spatial density interventions in stress-aware SIMP topology optimization, yielding comparable but statistically non-significant performance gains over rule-based baselines on 2D and 3D benchmarks.
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Isogeometric Topology Optimization Based on Topological Derivatives
An isogeometric topology optimization approach using topological derivatives and level-set methods in an immersed framework enables seamless geometry updates without remeshing and benefits from higher-order basis functions for solution accuracy.
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Sequential topology optimization: SIMP initialization for level-set boundary refinement
A sequential topology optimization approach uses SIMP results to initialize level-set refinement via signed distance function transfer on 3D meshes, achieving comparable compliance with up to 4.6x speedup on benchmarks.