{"total":5,"items":[{"citing_arxiv_id":"2607.01812","ref_index":26,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"TO-Master: an LLM-agent framework for automated topology optimization","primary_cat":"cs.CE","submitted_at":"2026-07-02T07:25:22+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"TO-Master is an LLM agent framework that orchestrates finite-element topology optimization from conversational inputs, supporting 2D/3D compliance, thermal, stress-constrained, and multi-load cases while reproducing benchmarks without user code.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.10509","ref_index":14,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"On the Localization of Checkerboarding in Multiaxial Stress Regions under SIMP Penalization","primary_cat":"cs.CE","submitted_at":"2026-06-09T07:36:49+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Checkerboarding under SIMP with linear elements localizes to multiaxial load-transfer regions as a discrete stiff substitute for penalized continuous intermediate densities, while uniaxial regions remain free of the pattern.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.19536","ref_index":14,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"A Dual Physics-Informed Kolmogorov-Arnold Neural Network Framework for Continuum Topology Optimization","primary_cat":"cs.CE","submitted_at":"2026-05-19T08:37:15+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Dual HRKAN framework (DPIKAN-TO) for topology optimization with one network predicting displacements and another handling sensitivity-based design updates.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.04735","ref_index":27,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Sequential topology optimization: SIMP initialization for level-set boundary refinement","primary_cat":"cs.CE","submitted_at":"2026-05-06T10:33:17+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":4.0,"formal_verification":"none","one_line_summary":"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.","context_count":1,"top_context_role":"background","top_context_polarity":"support","context_text":"compliance values reported in the following sections are computedfromgeometriesextractedusingMarchingCubes and discretized withTetGen(Section 3.6). This extraction- based evaluation is necessary because the SIMP compli- ance includes contributions from penalized intermediate- density elements that are absent in the extracted geometry, making direct comparison misleading [27]. Although the level set provides a sharp geometric boundary, the ersatz- materialapproachapproximatesthestiffnessofcutelements (those intersected by the zero level set) by weighting it with their solid volume fraction [21], producing intermediate- density contributions analogous to those in SIMP but con- fined to a narrow band around the interface [41]."},{"citing_arxiv_id":"2603.25099","ref_index":30,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Large Language Models as Optimization Controllers: Adaptive Continuation for SIMP Topology Optimization","primary_cat":"cs.CE","submitted_at":"2026-03-26T07:14:31+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}