From the 1 of 6 linked papers with an AI index.
6 papers
When a positive SIMP density floor is not enough: solver admissibility and guarded floor selection in matrix-free 3D topology optimization
Shaoliang Yang, Jun Wang, Yunsheng Wang
The paper identifies reliability issues in matrix‑free geometric‑multigrid FGMRES solvers for 3D SIMP topology optimization and proposes a verified floor‑selection policy that uses…
Empirical Calibration and Conditional-Reliability Diagnostics for Bearing RUL Prediction under Operating-Regime Shift
Shaoliang Yang, Jun Wang, Yunsheng Wang
Remaining useful life (RUL) estimates support reliability and maintenance decisions only if both point accuracy and prediction intervals remain trustworthy when operating condition…
IterSIMP-Ï: Evaluating LLM-Assisted Spatial Interventions in Stress-Aware Topology Optimization
Shaoliang Yang, Jun Wang, Yunsheng Wang
This paper studies whether multimodal large language models (LLMs) can serve as inspectable spatial proposal modules for stress-aware topology optimization. IterSIMP-Ï keeps the S…
A Matrix-Free Galerkin Multigrid Solver and Failure-Mode Screen for Single-GPU 3D SIMP Linear Systems
Shaoliang Yang, Jun Wang, Yunsheng Wang
Large 3D SIMP studies require repeated elasticity solves for density-dependent operators whose finest matrices are expensive to assemble and whose conditioning degrades under high…
Matrix-Free 3D SIMP Topology Optimization with Fused Gather-GEMM-Scatter Kernels
Shaoliang Yang, Jun Wang, Yunsheng Wang
The matrix-free gather-batched-GEMM-scatter pattern eliminates global stiffness assembly for three-dimensional SIMP topology optimization, but the conventional three-stage implemen…
AutoSiMP: Autonomous Topology Optimization from Natural Language via LLM-Driven Problem Configuration and Adaptive Solver Control
Shaoliang Yang, Jun Wang, Yunsheng Wang
We present AutoSiMP, an autonomous pipeline that transforms a natural-language structural problem description into a validated, binary topology without manual configuration. The pi…