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.
3D Topology Optimization using Convolutional Neural Networks
4 Pith papers cite this work, alongside 53 external citations. Polarity classification is still indexing.
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abstract
Topology optimization is computationally demanding that requires the assembly and solution to a finite element problem for each material distribution hypothesis. As a complementary alternative to the traditional physics-based topology optimization, we explore a data-driven approach that can quickly generate accurate solutions. To this end, we propose a deep learning approach based on a 3D encoder-decoder Convolutional Neural Network architecture for accelerating 3D topology optimization and to determine the optimal computational strategy for its deployment. Analysis of iteration-wise progress of the Solid Isotropic Material with Penalization process is used as a guideline to study how the earlier steps of the conventional topology optimization can be used as input for our approach to predict the final optimized output structure directly from this input. We conduct a comparative study between multiple strategies for training the neural network and assess the effect of using various input combinations for the CNN to finalize the strategy with the highest accuracy in predictions for practical deployment. For the best performing network, we achieved about 40% reduction in overall computation time while also attaining structural accuracies in the order of 96%.
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2026 4verdicts
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A hybrid-conditioned diffusion transformer generates 2D topologies matching SIMP solutions within 1% compliance error using only five denoising steps.
A transformer model with self-attention and auxiliary physics losses learns a direct non-iterative mapping from loads and fields to manufacturable optimized topologies.
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.
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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Diffusion Transformers with Hybrid Conditioning for Structural Optimization
A hybrid-conditioned diffusion transformer generates 2D topologies matching SIMP solutions within 1% compliance error using only five denoising steps.
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Physics-Informed Transformer for Real-Time High-Fidelity Topology Optimization
A transformer model with self-attention and auxiliary physics losses learns a direct non-iterative mapping from loads and fields to manufacturable optimized topologies.
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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.