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.
Woldseth, Niels Aage, Jakob Andreas Bærentzen, and Ole Sigmund
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
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.
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.