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cs.CE2026
SymbolicLM: Training Language Models as Symbolic Regressors
Jun Yao, Yingfan Hua, Ruikun Li +4
Large Language Models (LLMs) have shown promising capabilities in scientific reasoning, yet scientific discovery ultimately requires deriving precise laws directly from observation…
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
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…