A diffusion model with hierarchical physics-feature conditioning and a thermal-conduction-inspired connectivity loss reduces compliance error and floating material in topology optimization.
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MH-PINN compactifies unbounded domains with mapping and enforces wave boundary conditions through network architecture for efficient, accurate simulations.
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HPG-Diff: Hierarchical physics-guided diffusion with differentiable connectivity constraints for topology optimization
A diffusion model with hierarchical physics-feature conditioning and a thermal-conduction-inspired connectivity loss reduces compliance error and floating material in topology optimization.
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Mapping-based Hard-constrained Physics-Informed Neural Networks for unbounded wave problems
MH-PINN compactifies unbounded domains with mapping and enforces wave boundary conditions through network architecture for efficient, accurate simulations.