论文标题
VPNET：具有学习无源动力学的批量保护神经网络
VPNets: Volume-preserving neural networks for learning source-free dynamics
论文作者
论文摘要
我们提出了使用轨迹数据来学习未知无源动态系统的音量扩展网络（VPNET）。我们提出了三个模块，并将它们组合在一起以获得两个网络体系结构，即创建的R-VPNET和LA-VPNET。所提出的模型的独特特征是它们具有内在的体积保护。另外，证明了相应的近似定理，从理论上讲，这些定理可以保证所提出的VPNET的表达性，以学习无源的动力学。数值实验证明了VP-NET的有效性，概括能力和结构保存特性。
We propose volume-preserving networks (VPNets) for learning unknown source-free dynamical systems using trajectory data. We propose three modules and combine them to obtain two network architectures, coined R-VPNet and LA-VPNet. The distinct feature of the proposed models is that they are intrinsic volume-preserving. In addition, the corresponding approximation theorems are proved, which theoretically guarantee the expressivity of the proposed VPNets to learn source-free dynamics. The effectiveness, generalization ability and structure-preserving property of the VP-Nets are demonstrated by numerical experiments.
