3 citations · 5 across the 13 of their papers we have counts for
13 papers
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…
Symbolic Regression-Enhanced Dynamic Wake Meandering: Fast and Physically Consistent Wind-Turbine Wake Modeling
Ding Wang, Dachuan Feng, Kangcheng Zhou +3
Accurately modeling wind turbine wakes is essential for optimizing wind farm performance but remains a persistent challenge. While the dynamic wake meandering (DWM) model captures…
An explainable operator approximation framework under the guideline of Green's function
Jianghang Gu, Ling Wen, Yuntian Chen +1
Traditional numerical methods, such as the finite element method and finite volume method, adress partial differential equations (PDEs) by discretizing them into algebraic equation…
Discovery of Green's function based on symbolic regression with physical hard constraints
Jianghang Gu, Mengge Du, Yuntian Chen +1
The Green's function, serving as a kernel function that delineates the interaction relationships of physical quantities within a field, holds significant research implications acro…
Quantifying In-Context Reasoning Effects and Memorization Effects in LLMs
Siyu Lou, Yuntian Chen, Xiaodan Liang +2
In this study, we propose an axiomatic system to define and quantify the precise memorization and in-context reasoning effects used by the large language model (LLM) for language g…
Empowering Machines to Think Like Chemists: Unveiling Molecular Structure-Polarity Relationships with Hierarchical Symbolic Regression
Siyu Lou, Chengchun Liu, Yuntian Chen +1
Thin-layer chromatography (TLC) is a crucial technique in molecular polarity analysis. Despite its importance, the interpretability of predictive models for TLC, especially those d…
Transforming organic chemistry research paradigms: moving from manual efforts to the intersection of automation and artificial intelligence
Chengchun Liu, Yuntian Chen, Fanyang Mo
Organic chemistry is undergoing a major paradigm shift, moving from a labor-intensive approach to a new era dominated by automation and artificial intelligence (AI). This transform…