arXiv:2608. 14615v1 Announce Type: new Abstract: Large Language Models (LLMs) perform well on established code-generation and mathematical-reasoning benchmarks, but their capabilities in mechanics and spatial geometry, here denoted as mechanical engineering awareness, has not been quantified systematically.
arXiv:2609.38089v1 Announce Type: cross Abstract: Ship structural vibrations contribute to noise, fatigue, and equipment damage, while dynamic-compliance topology optimization can produce pathologica...
arXiv:2608. 09296v1 Announce Type: new Abstract: A CAD model is not engineering-grade merely because it looks correct.
arXiv:2606. 17696v1 Announce Type: new Abstract: Parametric computer-aided design records both final geometry and the ordered construction history that determines how a part can be edited.
arXiv:2608.21976v1 Announce Type: new Abstract: Agentic AI has automated parts of scientific discovery, including paper generation, expert-level coding, therapeutic proposal, and autonomous experimen...
arXiv:2604. 25241v2 Announce Type: replace Abstract: Categorical structural optimization under aleatoric uncertainty is challenging because each design variable must be selected from a finite catalog of admissible instances, while each candidate design may require expensive stochastic finite-element evaluations.
MidSurfNet is a learning-based method for pairing faces in thin‑walled CAD models, addressing the key subproblem of mid‑surface abstraction for finite element analysis. It uses a dual‑stream scorer—one geometry stream and one attributed‑topology stream—fused by a pair‑conditioned gate to make per‑pair decisions that replace manual thresholds. The method organizes retained relations into variable‑cardinality face groups independent of processing order, and on a new MidSurf dataset of 1,575 annotated models it achieves an 87.32% pair‑level F1‑score and a 75.42% completion rate, outperforming rule‑based baselines, especially on multi‑wall‑thickness cases.
The paper introduces a method that integrates a frozen text‑to‑image diffusion model into density‑based topology optimization via score distillation sampling. By converting a natural language prompt into a generative gradient, the approach lets physics decide which design features survive, achieving significant compliance reductions across multiple domains and physics regimes. An automated pipeline then transforms the optimized density fields into CAD‑ready geometries.
The monograph introduces Elimination Geometry (EG), a typed, native‑loss, audit‑oriented framework that investigates when locally optimal objects can be realized by a shared deployment rule. EG examines how elimination and compression can erase distinctions needed for prediction, inference, control, or representation, and it separates local solvability, global realizability, and finite‑sample certifiability. The work synthesizes tools from geometry, optimization, information theory, statistics, and machine learning to address regular, coordination, singular, compositional, and resource‑limited mechanisms, and demonstrates applications in sparse model selection, distribution‑free prediction, observational treatment policies, routed expert and retrieval systems, and learned score fields.
arXiv:2503. 17386v2 Announce Type: replace-cross Abstract: Crashworthiness is a key performance measure in the design of safety-critical vehicle panel components such as B-pillars.
arXiv:2607. 05573v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) and Vision-Language Models (VLMs) enable the automatic generation of parametric 3D designs from natural-language specifications.
arXiv:2602. 10392v2 Announce Type: replace Abstract: When designing new materials, it is often necessary to tailor the material design to have some desired properties.