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部分微分方程与深度神经网络相遇:一项调查
IEEE transactions on neural networks and learning systems
|March 14, 2025
概括
深度学习 (DL) 为解决复杂的局部微分方程 (PDEs) 的传统数值方法提供了强大的替代方案. 这项调查系统地审查了各种深度神经网络 (NN) 方法,用于各种科学领域的PDE解决方案.
科学领域:
- 计算科学与工程 计算科学与工程
- 应用数学 应用数学 应用数学
- 科学计算是科学计算.
背景情况:
- 部分微分方程 (PDEs) 模型关键的科学和工程问题.
- 传统的PDE数值方法面临着计算效率低下的挑战.
- 深度学习 (DL) 为解决PDEs提供了一个有希望的替代方案.
研究的目的:
- 系统地审查和分类深度神经网络 (NN) 解决PDE的方法.
- 提供对PDE的DL目前进展的全面概述.
- 弥合现有文献的差距,提供更广泛的分类学,超越像物理信息神经网络 (PINNs) 这样的特定方法.
主要方法:
- 对PDE应用的多种深度神经网络 (NN) 架构的分类和审查.
- 分析跨科学,工程和医疗领域的应用.
- 对PDE的DL历史概述,包括关键挑战和未来趋势.
主要成果:
- 深度神经网络 (DNN) 显示出作为PDE有效解决者的巨大潜力.
- 目前正在开发和应用各种各样的NN方法来解决PDE问题.
- 该调查提供了对解决PDEs中的DL格局的结构化理解.
结论:
- 深度学习为解决部分微分方程 (PDEs) 提供了一种变革性的方法.
- 这项调查为研究人员和从业人员探索用于PDE应用的DL提供了宝贵的资源.
- 未来的趋势表明,在科学和工程领域,基于 NN 的方法将继续取得进展,并得到更广泛的采用.
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