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.
S., Sigmund O
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EngiAI introduces a LangGraph-based multi-agent framework and a three-part benchmark suite for LLM-driven engineering design, reporting high task completion rates for proprietary models on Beams2D and Photonics2D problems.
sGPO uses an initial-policy success-rate profiling pass to adaptively set rollout group sizes, filter data, and build a curriculum, cutting total RLVR training compute by 3x while matching baseline performance.
Dual HRKAN framework (DPIKAN-TO) for topology optimization with one network predicting displacements and another handling sensitivity-based design updates.
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.
An LLM acting as real-time controller for SIMP topology optimization parameters outperforms fixed schedules and heuristics, delivering 5.7-18.1% lower compliance on 2D and 3D benchmarks.
Method for generating minimum-weight structurally robust shell objects from 3D models using Laplacian parametrization to ensure intersection-free inner boundaries during stress-based thickness optimization.
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.
STORX is an open-source object-oriented MATLAB framework integrating parametric and level-set shape optimization with density, level-set, evolutionary, and Pareto-tracing topology optimization methods.
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.
citing papers explorer
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EngiAI: A Multi-Agent Framework and Benchmark Suite for LLM-Driven Engineering Design
EngiAI introduces a LangGraph-based multi-agent framework and a three-part benchmark suite for LLM-driven engineering design, reporting high task completion rates for proprietary models on Beams2D and Photonics2D problems.
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Sequential topology optimization: SIMP initialization for level-set boundary refinement
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.