3 citations · 5 across the 13 of their papers we have counts for
7 papers · 1 filter
Efficient Constant Optimization for Symbolic Regression with GPU-Accelerated Tree-Based Genetic Programming
Hao Mao, Xu Tony Liu, Shuai Lu +3
Constant optimization refines the numerical coefficients of candidate expressions in tree-based genetic programming for symbolic regression. But its per-generation cost has led mod…
ArtisanCAD: An Industrial-Level CAD Agent with Expert-Grounded Knowledge Distillation
Yunhan Xu, Qifeng Wu, Xunjin Li +10
Computer-aided design (CAD) for industrial components requires long-horizon procedural modeling, robust feature dependencies, editable parametric geometry, and production-grade B-R…
Knowledge-Constrained Shape Optimization with a Mixture-of-Experts Neural Operator for High-Confidence Design
Wenhao Fan, Yuanwei Bin, Jianghan Gu +4
Engineering shape optimization faces challenges in both expert-dependent problem setup and surrogate-model reliability. In practical aerodynamic design, optimization settings such…
Cross-LLM Consistency in Inference: Evidence from Shared Interactions
Siyu Lou, Yao Yan, Yuntian Chen +1
Large language models (LLMs) differ in architecture, training data, and optimization procedures, yet they may still develop similar internal inference patterns. In this paper, we e…
A Geometry-Aware Triplane Field Network for Vehicle Aerodynamic Prediction
Kangkang Qi, Huiyu Yang, Keqi Ding +5
High-fidelity computational fluid dynamics (CFD) is crucial to vehicle aerodynamic analysis, but its cost still constrains early-stage design exploration. Machine-learning-based su…
RETO: A Rotary-Enhanced Transformer Operator for High-Fidelity Prediction of Automotive Aerodynamics
Bojun Zhang, Huiyu Yang, Yunpeng Wang +4
Rapid aerodynamic evaluation is crucial for modern vehicle design, yet existing neural operators struggle to capture intricate spatial correlations. We propose the rotary-enhanced…
GENSR: Symbolic Regression Based in Equation Generative Space
Qian Li, Yuxiao Hu, Juncheng Liu +1
Symbolic Regression (SR) tries to reveal the hidden equations behind observed data. However, most methods search within a discrete equation space, where the structural modification…