ordinary-differential-equations

该论文提出 Continuum-LM，将预训练残差变换复用为连续深度语言模型中的动力学算子，通过 ODE 数值积分控制计算量，在八项科学任务上较离散基线分别在 0.6B 和 1.7B 规模提升 7.77 和 10.96 个百分点的平均准确率。

This paper introduces MEDA, an LLM- and symbolic regression-powered agentic framework for automatically discovering ordinary differential equation (ODE) models of biological dynamical systems. It retrieves background knowledge, proposes candidate ODEs, and evaluates them across canonical model retrieval, extrapolation, and open-ended discovery tasks, demonstrating strong structural recovery and biologically plausible models.

PyCC.id is a Python library for hypothesis-driven equation discovery from time-series data, leveraging structural identifiability to help filter candidate models.

This paper introduces DoLQ, a multi-agent framework that uses Large Language Models to perform both qualitative and quantitative evaluations for discovering ordinary differential equations from observational data.