通过隐形空间表示在PINNs中推进概括
IEEE transactions on neural networks and learning systems
|August 25, 2025
概括
基于物理的动态表示学习器 (PiDo) 增强了部分微分方程 (PDEs) 的神经网络概括. 这种新的方法学习了潜在的动态,改善了各种PDE配置的性能,并使新的应用成为可能.
科学领域:
- 计算科学
- 应用数学
- 机器学习
背景情况:
- 基于物理学的神经网络 (PINNs) 是有效的模拟由部分微分方程 (PDEs) 控制的动态系统.
- 然而,现有的PINN在不同的场景中表现出有限的概括能力,例如不同的初始条件或PDE系数.
研究的目的:
- 介绍一种新的基于物理的神经PDE解决器,即基于物理的动态表示学习器 (PiDo),旨在在各种PDE配置中进行增强的概括.
- 在基于物理的框架中整合潜在动力学模型,提高优化和稳定性的挑战.
主要方法:
- 使用自动解码将PDE解决方案投射到潜伏空间中,以利用共享的动态系统结构.
- 它学习了PDE系数的潜在表示动态.
- 新的规范化技术用于诊断和缓解潜伏空间中的优化困难.
主要成果:
- 在不同的初始条件,PDE系数和培训时间范围内,PiDo表现出有效的概括性.
- 这种方法显示时间推断性能提高,训练稳定性提高.
- 在1D组合方程和2D纳维埃-斯托克方程上验证.
结论:
- PiDo提供了基于物理的PDE解决的强大框架,具有卓越的概括能力.
- 学习的表现可以转移到下游任务,如长期集成和反向问题.
- 开发的规范化策略有效地解决了潜在空间物理信息学习中的优化挑战.
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