机器学习对部分微分方程的有希望的方向
Steven L Brunton1, J Nathan Kutz2
1Department of Mechanical Engineering, University of Washington, Seattle, WA, USA. sbrunton@uw.edu.
机器学习加速了部分微分方程 (PDEs) 的研究. 它有助于发现新的PDEs,简化复杂的系统,并增强物理和工程应用的数值方法.
科学领域:
- 计算物理和应用数学应用数学
- 机器学习在科学中的应用
背景情况:
- 部分微分方程 (PDEs) 是描述自然现象和复杂系统的基础.
- 分析和解决PDE的传统方法面临着多尺度物理和复杂系统的挑战.
研究的目的:
- 探索机器学习 (ML) 是如何推进部分微分方程 (PDEs) 研究.
- 突出ML在发现治理PDEs,开发减少顺序模型和改进数值算法方面的作用.
主要方法:
- 审查基于ML的方法来发现新的PDEs和粗的近似值.
- 检查ML技术学习坐标系统和PDE分析的减少顺序模型.
- 调查ML方法来表示解决方案操作员和增强数值PDE解答器.
主要成果:
- 机器学习提供了新的方法来发现复杂系统的治理方程.
- ML有助于创建有效的坐标系统和减少顺序模型,简化PDE分析.
- 基于ML的方法在改善PDE的解决方案运算符和数值算法的表示方面表现有前途.
结论:
- 机器学习是PDE研究中的变革性工具,为发现和分析提供了新的途径.
- 已经取得了重大进展,但在充分利用ML来解决复杂的PDE问题方面,仍然存在挑战.
- 未来的机遇在于进一步整合机器学习,推动科学建模和计算的边界.
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