Nonlinear Dynamics - A new method named bilinear neural network is introduced in this paper, and the corresponding tensor formula is proposed to obtain the exact analytical solutions of nonlinear... 相似文献
Spherical Li-rich lithium manganese oxide(LMO) spinel material was synthesized by an ion implanted method assisted by polyalcohol doped with Niobium and Phosphate simultaneously.The material was characterized by scanning electron microscopy,X-ray diffraction and BET specific surface area analysis.The electrochemical performances were investigated with galvanostatic techniques and cyclic voltammetry.The synthesis process was investigated with TG/DSC.The results show that the lithium ion can be immersed into the pore of manganese dioxide at a low temperature with the ion implanted method.The prepared materials have a higher discharge capacity and better crystallization than those prepared by solid phase method.The doped Nb can improve the capacity of the Li-rich LMO spinel and reinforce the crystal growth along(111) and(400) planes.The crystal grains show circular and smooth morphology,which makes the specific surface area greatly decreased.Phosphate-doped LMO spinel exhibits good high-rate capacity and structure stability.The prepared Li_(1.09)Mn_(1.87)Nb_(0.031)O_(3.99)(PO_4)_(0.021)delivers a discharge capacity of 119mAhg~(-1) at 0.2C(1C=148mAg~(-1)) and 112.8 mAhg~(-1) at 10 C,the discharge capacity retention reaches 98% at 1 ℃ after 50 cycles at 25 ℃ and 94% at 55 ℃. 相似文献
Although many effective methods for solving partial differential equations (PDEs) have been proposed, there is no universal method that can solve all PDEs. Therefore, solving partial differential equations has always been a difficult problem in mathematics, such as deep neural network (DNN). In recent years, a method of embedding some basic physical laws into traditional neural networks has been proposed to reveal the dynamic behavior of equations directly from space-time data [i.e., physics-informed neural network (PINN)]. Based on the above, an improved deep learning method to recover the new soliton solution of Huxley equation has been proposed in this paper. As far as we know, this is the first time that we have used an improved method to study the numerical solution of the Huxley equation. In order to illustrate the advantages of the improved method, we use the same network depth, the same hidden layer and neurons contained in the hidden layer, and the same training sample points. We analyze the dynamic behavior and error of Huxley’s exact solution and the new soliton solution and give vivid graphs and detailed analysis. Numerical results show that the improved algorithm can use fewer sample points to reconstruct the exact solution of the Huxley equation with faster convergence speed and better simulation effect.
In this paper, an extended simplest equation method is proposed to seek exact travelling wave solutions of nonlinear evolution equations. As applications, many new exact travelling wave solutions for several forms of the fifth-order KdV equation are obtained by using our method. The forms include the Lax, Sawada-Kotera, Sawada-Kotera-Parker-Dye, Caudrey-Dodd-Gibbon, Kaup-Kupershmidt, Kaup-Kupershmidt-Parker-Dye, and the Ito forms. 相似文献
Journal of Radioanalytical and Nuclear Chemistry - Metal-organic frameworks (MOFs) MIL-101(Cr)-PMIDA with phosphate groups were prepared for the adsorption of U(VI). The morphology and structure of... 相似文献