物理信息神经网络

PINNs are a class of physics-informed machine learning methods that embed the governing physical laws (e.g., partial differential equations) directly into the loss function of a neural network. This allows the network to learn the solution to the physical problem while minimizing the residual of the governing equations, enabling high-fidelity modeling without requiring extensive labeled data. / 物理信息神经网络是一种物理信息机器学习方法，它将控制物理定律（如偏微分方程）直接嵌入神经网络的损失函数中。这使得网络能够在最小化控制方程残差的同时学习物理问题的解，从而实现高保真建模，而无需大量标记数据。

Knowledge type

数学理论

First proposed or discovered

Proposer

Turkey Farm member

Creator

农场主

Last updated

2026-10-28 19:03:08