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- 物理信息神经网络（PINN）: 将物理知识融合到深度学习中-CSDN博客
与传统的数据驱动的神经网络不同，PINNs 在学习过程中利用物理法则对模型进行指导，从而提高模型泛化能力，特别是在数据较少或噪声较大的情况下。
- Physics-informed neural networks (PINNs)入门介绍 - 知乎
PINNs定义：physics-informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear partial differential equations
- Physics-informed neural networks - Wikipedia
PINNs allow for addressing a wide range of problems in computational science and represent a pioneering technology leading to the development of new classes of numerical solvers for PDEs
- GitHub - maziarraissi PINNs: Physics Informed Deep Learning: Data . . .
It is highly recommended to utilize implementations of Physics-Informed Neural Networks (PINNs) available in PyTorch, JAX, and TensorFlow v2 We introduce physics informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear partial
- PINNS · 上海财经大学人工智能案例平台
PINNs是一种将物理定律直接融入神经网络架构的深度学习模型。 传统神经网络主要基于数据训练，学习数据中的模式。 而PINNs更进一步，在训练过程中同时考虑物理规律。 例如，在求解偏微分方程（PDEs）时，PINNs把描述物理现象的PDE作为约束条件。
- What Are Physics-Informed Neural Networks (PINNs)?
PINNs are a class of physics-informed machine learning methods that seamlessly integrate physics knowledge with data Often, PINNs get compared with purely data-driven methods and traditional numerical methods for solving problems involving PDEs and ODEs
- Nature大杀器！PINN物理信息神经网络 - 知乎
物理信息神经网络 （Physics-Informed Neural Networks, PINNs）作为一种融合了物理定律与深度学习的新型建模方法，近年来在科学计算和工程领域取得了突破性进展。
- From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning
Physics-Informed Neural Networks (PINNs) have emerged as a key tool in Scientific Machine Learning since their introduction in 2017, enabling the efficient solution of ordinary and partial differential equations using sparse measurements
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