AI RESEARCH
LLM-ODE: Data-driven Discovery of Dynamical Systems with Large Language Models
arXiv CS.LG
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ArXi:2603.20910v1 Announce Type: new Discovering the governing equations of dynamical systems is a central problem across many scientific disciplines. As experimental data become increasingly available, automated equation discovery methods offer a promising data-driven approach to accelerate scientific discovery. Among these methods, genetic programming (GP) has been widely adopted due to its flexibility and interpretability. However, GP-based approaches often suffer from inefficient exploration of the symbolic search space, leading to slow convergence and suboptimal solutions.