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通过从数学手册中学习,对部分微分方程的生成性发现
Hao Xu1,2, Yuntian Chen3,4, Rui Cao5,6
1Zhejiang Key Laboratory of Industrial Intelligence and Digital Twin, Eastern Institute of Technology, Ningbo, Zhejiang, PR China.
Nature communications
|November 21, 2025
概括
本研究介绍了EqGPT,这是一种以知识为导向的AI模型,通过从数学手册中学习来发现部分微分方程 (PDEs). 它有效地从数据中发现复杂的方程,推进科学发现.
科学领域:
- 计算物理 计算物理
- 应用数学 应用数学 应用数学
- 科学机器学习科学机器学习
背景情况:
- 数据驱动的部分微分方程 (PDEs) 的发现对于理解复杂系统至关重要.
- 纯数据驱动的方法难以平衡搜索空间和优化效率.
- 结合现有知识可以增强PDE发现.
研究的目的:
- 开发一种以知识为导向的方法来发现部分微分方程 (PDEs).
- 提高数据驱动PDE发现的效率和准确性,特别是在复杂方程中.
- 为了证明框架对现实世界科学问题的适用性.
主要方法:
- 一个以知识为导向的框架,使用基于手册PDEs的生成模型 (EqGPT) 进行培训.
- 将PDEs编码为运算符和术语的类似句子结构.
- 实施一个"生成-评估-优化"循环,用于自主PDE识别.
主要成果:
- 该框架准确有效地恢复各种PDE形式,包括那些具有复杂的时间或空间术语的形式.
- 证明了对不规则的空间领域和更高维度的概括性.
- 从实验数据中成功发现了一种用于强非线性表面重力波的新型PDE.
结论:
- 以知识为导向的方法显著提高了数据驱动的PDE发现.
- EqGPT提供了一个强大的工具来揭示复杂的物理定律,并支持实际的科学发现.
- 该方法在解决各种科学领域的挑战性问题方面具有前景.
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