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.
Bendsøe and Ole Sigmund.Topology Optimization
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SiMPL generates feasible iterates for multi-material topology optimization by using tailored Bregman divergences to enforce point-wise polytopal design constraints, with global constraints handled via a small dual problem.
Mosaic is a benchmark suite evaluating 14 differentiable PDE solvers across fluids, structures, and heat transfer, showing large variations in cost and conditioning but similar convergence behavior.
A time-discrete variational model for accretive surface growth minimizes mean compliance subject to a global mass constraint and yields a constrained gradient flow in the continuous-time limit.
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.
Generalized lattice-network method relaxes symmetric inductance condition for cancelling capacitor parasitic inductance and applies it to common-mode noise reduction with experimental checks.
IterSIMP-σ integrates multimodal LLMs for proposing spatial density interventions in stress-aware SIMP topology optimization, yielding comparable but statistically non-significant performance gains over rule-based baselines on 2D and 3D benchmarks.
A sequential topology optimization approach uses SIMP results to initialize level-set refinement via signed distance function transfer on 3D meshes, achieving comparable compliance with up to 4.6x speedup on benchmarks.
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