Dual HRKAN framework (DPIKAN-TO) for topology optimization with one network predicting displacements and another handling sensitivity-based design updates.
An efficient 3D topology optimization code written in Matlab
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
method 2
citation-polarity summary
years
2026 3verdicts
UNVERDICTED 3roles
method 2polarities
use method 2representative citing papers
A new coupled adjoint enables efficient linear stability analysis as a constraint in transonic buffet-constrained aerodynamic shape optimization, demonstrated with 22.4% drag reduction on the OAT15A airfoil.
A fused gather-GEMM-scatter CUDA kernel achieves 4.6-7.3x end-to-end speedup and 3.2-4.9x lower energy for matrix-free 3D SIMP topology optimization on RTX 4090 compared to three-stage baselines.
citing papers explorer
-
A Dual Physics-Informed Kolmogorov-Arnold Neural Network Framework for Continuum Topology Optimization
Dual HRKAN framework (DPIKAN-TO) for topology optimization with one network predicting displacements and another handling sensitivity-based design updates.
-
Buffet Alleviation via Linear Stability Adjoint
A new coupled adjoint enables efficient linear stability analysis as a constraint in transonic buffet-constrained aerodynamic shape optimization, demonstrated with 22.4% drag reduction on the OAT15A airfoil.
-
Matrix-Free 3D SIMP Topology Optimization with Fused Gather-GEMM-Scatter Kernels
A fused gather-GEMM-scatter CUDA kernel achieves 4.6-7.3x end-to-end speedup and 3.2-4.9x lower energy for matrix-free 3D SIMP topology optimization on RTX 4090 compared to three-stage baselines.