Verifying Physics-Informed Neural Network Fidelity using Classical Fisher Information from Differentiable Dynamical System
通过可微动力学系统中的经典Fisher信息验证物理信息神经网络的保真度
机构 * lisha (Software/Hardware Integration Lab)（lisha（软件/硬件集成实验室））
AI总结 本文提出通过经典Fisher信息验证PINN保真度的方法，利用可微动力学系统分析其在物理建模中的有效性。
Comments This paper has been submitted and is currently under review at IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
