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.
Engineering Applications of Artificial Intelligence , volume =
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High-order topological charges up to ±3 are realized in C4-symmetric photonic crystal slabs via parameter-driven merging of off-Γ BICs using a unified geometric framework based on Fabry-Pérot interference and guided resonances.
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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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Unified Topological Dynamics of Merging Bound States in the Continuum for High-Order Topological Charges
High-order topological charges up to ±3 are realized in C4-symmetric photonic crystal slabs via parameter-driven merging of off-Γ BICs using a unified geometric framework based on Fabry-Pérot interference and guided resonances.