1.
A New Class of Efficient Numerical Methods for Nonlinear Stiff Differential Equations
非线性刚性微分方程一类新的高效数值方法
2.
Efficient Discontinuous Galerkin Methods for Miscible Displacement Problems;
渗流驱动问题间断有限元高效数值方法研究
3.
Highly Efficient Numerical Methods for Stiff Differential Equations and Analysis of Numerical Stabilities for Neutral Functional Differential Equations
刚性微分方程几类高效数值方法及中立型泛函微分方程数值稳定性分析
4.
Numerical tests indicate that PTAM is accurate and efficient.
数值试验表明，本文方法是准确和高效的。
5.
Efficient Method for Simulation of Viscous Flows Past Helicopter Rotors and Active Flow Control;
旋翼绕流的高效数值计算方法及主动流动控制研究
6.
The High-effective D esign Method for Chlorine Compressor and Analysis of Numerical Simulation Result
氯气压缩机的高效设计方法及数值模拟结果分析
7.
Study on Efficient Numerical Methods and Implementation for Highly Oscillatory Integrals
高振荡函数积分的高效数值算法及实现研究
8.
On Improving HSI Noise Prediction for Helicopter Rotor in Forward Flight When Flowfield is Partially near Sonic Speed
旋翼前飞气动噪声高效数值算法研究
9.
Efficient numerical approach to EXIT curves of accumulate code
累积码EXIT曲线的高效数值求法
10.
High Order Accurate Numerical Methods for Cahn-Hilliard Equations
Cahn-Hilliard方程的高精度数值方法
11.
Numerical simulations validate the effectiveness of the method.
数值模拟结果验证了方法的有效性。
12.
The numerical result showed that the method is effective.
数值分析结果表明了该方法的有效性。
13.
Numerical Studies on the Crack Closure Effect in Fatigue Crack Propagation;
疲劳裂纹闭合效应数值模拟方法研究
14.
Study on Improving the Numerical Optimization Efficiency of Real-coded Genetic Algorithms;
提高实数遗传算法数值优化效率的研究
15.
Approach and Method of Improving Classroom-effect of College Mathematics Teaching;
提高高校数学教学效果的途径和方法
16.
High Order Multiple Method for Fractional Ordinary Differential Equation and Numerical Method for Variable Order Fractional Diffusion Equation;
分数阶常微分方程的高阶多步法和变分数阶扩散方程的数值方法
17.
Comparing radix method-efficient approach to compute core of information table
基数比较法—高效的信息表求核方法
18.
By comparison with the traditional method for data mining,this method is more effective,more accurate,and more accordant to practice.
与传统的数据挖掘方法相比较,区间值聚类的数据挖掘模型更加高效、准确、符合实际。