首页|Structure-Preserving Recurrent Neural Networks for a Class of Birkhoffian Systems
Structure-Preserving Recurrent Neural Networks for a Class of Birkhoffian Systems
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
万方数据
维普
In this paper,the authors propose a neural network architecture designed specifically for a class of Birkhoffian systems—The Newtonian system.The proposed model utilizes recurrent neural networks(RNNs)and is based on a mathematical framework that ensures the preservation of the Birkhoffian structure.The authors demonstrate the effectiveness of the proposed model on a variety of problems for which preserving the Birkhoffian structure is important,including the linear damped oscillator,the Van der Pol equation,and a high-dimensional example.Compared with the unstructured baseline models,the Newtonian neural network(NNN)is more data efficient,and exhibits superior generalization ability.
万方数据
维普
