对于部分微分方程的基础模型:多运算器学习和推断
Jingmin Sun1, Yuxuan Liu2, Zecheng Zhang3
1Carnegie Mellon University, Department of Mathematical Sciences, Pittsburgh, Pennsylvania 15213, USA.
Physical review. E
|April 18, 2025
概括
本研究介绍了PROSE-PDE,这是一个科学问题的多式联络基础模型. 它预测了时空系统的未来状态,并学习了管理方程,展示了强大的概括能力.
科学领域:
- 多模式基础模型的模型.
- 科学机器学习科学机器学习
- 部分微分方程 (PDEs) 是一个方程.
背景情况:
- 基础模型在语言和图像任务中表现出色.
- 预测时空系统和学习治理方程是关键的科学挑战.
研究的目的:
- 介绍PROSE-PDE,这是科学问题的多模式基础模型.
- 为时空系统实现双模式到双模式学习.
- 预测未来的状态,并学习潜在的物理方程.
主要方法:
- 多操作员学习方法.
- 培训一维依赖时间的非线性常数系数PDEs.
- 外推研究来评估概括.
主要成果:
- 通过强大的多操作员培训,PROSE-PDE将物理特征泛化.
- 该模型对未见的模型或数据进行外推,以预测PDE解决方案.
- 符号模式解决了定位问题,并增强了预测能力.
结论:
- PROSE-PDE为科学基础建模提供了一种全新的方法.
- 该模型显示了物理,地质和生物学应用的巨大潜力.
- 多模式学习增强了复杂的时空系统的预测.
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