从数据中学习可变PDEs的离散模型
Christian Offen1, Sina Ober-Blöbaum1
1Department of Mathematics, Paderborn University, Warburger Str. 100, 33098 Paderborn, Germany.
Chaos (Woodbury, N.Y.)
|January 8, 2024
概括
我们开发了一种机器学习方法,从数据中学习离散场理论. 这种结构保存方法确保了可靠的数值模拟,并识别了基本的简单解决方案,即使在培训数据中没有明确说明.
科学领域:
- 计算物理 计算物理
- 机器学习 机器学习
- 理论物理 理论物理
背景情况:
- 从观测数据中学习离散场理论对于理解复杂系统至关重要.
- 传统的方法可能很难识别数据中的潜在结构或简单的解决方案.
研究的目的:
- 开发一种新的机器学习框架,用于从时空数据中学习离散场理论.
- 确保学习理论的数值规律性和稳定性,以便进行高效的模拟.
- 为了能够识别结构简单的解决方案,如旅行波,即使在训练数据中缺席.
主要方法:
- 训练一个神经网络来建模一个离散的拉格朗的密度.
- 使用观测数据确保与离散欧勒-拉格朗日方程的一致性.
- 引入调整器以优化数值规律性并确保可靠的模拟.
- 展示波形方程和施罗丁格方程的方法.
主要成果:
- 一个结构保存机器学习架构,用于离散场理论.
- 导出调节器的技术,以提高数值稳定性和效率.
- 成功识别了在训练数据中不存在的简单解决方案 (例如,移动波).
- 与数据驱动模型订单减少进行比较,突出解决方案识别的优势.
结论:
- 提出的方法有效地从数据中学习离散场理论,产生强大而高效的模型.
- 该技术有助于发现基本解决方案,提供超越培训数据集的见解.
- 这种方法在物理学和相关领域推进了数据驱动的科学发现.
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