46 citations · 143 across the 152 of their papers we have counts for
5 papers · 1 filter
Pixel2Phys: Distilling Governing Laws from Visual Dynamics
Ruikun Li, Jun Yao, Yingfan Hua +5
Discovering physical laws directly from high-dimensional visual data is a long-standing human pursuit but remains a formidable challenge for machines, representing a fundamental go…
Chem-R: Learning to Reason as a Chemist
Weida Wang, Benteng Chen, Di Zhang +14
Although large language models (LLMs) have significant potential to advance chemical discovery, current LLMs lack core chemical knowledge, produce unreliable reasoning trajectories…
Finetuning Large Language Model as an Effective Symbolic Regressor
Yingfan Hua, Ruikun Li, Jun Yao +5
Deriving governing equations from observational data, known as Symbolic Regression (SR), is a cornerstone of scientific discovery. Large Language Models, (LLMs) have shown promise…
MLLM-based Discovery of Intrinsic Coordinates and Governing Equations from High-Dimensional Data
Ruikun Li, Yan Lu, Shixiang Tang +2
Discovering governing equations from scientific data is crucial for understanding the evolution of systems, and is typically framed as a search problem within a candidate equation…
AFBench: A Large-scale Benchmark for Airfoil Design
Jian Liu, Jianyu Wu, Hairun Xie +8
Data-driven generative models have emerged as promising approaches towards achieving efficient mechanical inverse design. However, due to prohibitively high cost in time and money,…