4 papers
Mastering Olympiad-Level Physics with Artificial Intelligence
Dong-Shan Jian, Xiang Li, Chen-Xu Yan +10
Olympiad-level physics problem-solving significantly challenges both humans and artificial intelligence (AI), as it requires integrating appropriate modeling, application of physic…
AI-Newton: A Concept-Driven Physical Law Discovery System without Prior Physical Knowledge
You-Le Fang, Dong-Shan Jian, Xiang Li +1
While current AI-driven methods excel at deriving empirical models from individual experiments, a significant challenge remains in uncovering the common fundamental physics that un…
LOCA: Logical Chain Augmentation for Scientific Corpus Cleaning
You-Le Fang, Dong-Shan Jian, Xiang Li +5
While Large Language Models (LLMs) excel in general domains, their reliability often falls short in scientific problem-solving. The advancement of scientific AI depends on large-sc…
Efficient Computation of One-Loop Feynman Integrals and Fixed-Branch Integrals to High Orders in
Rui-Jun Huang, Dong-Shan Jian, Yan-Qing Ma +2
We propose a novel method, called the dimension-changing transformation (DCT), to compute one-loop Feynman integrals and recently introduced fixed-branch integrals to arbitrary ord…