3 papers
cs.CE2026
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…
cs.CE2025
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…
cs.CE2025
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…