Sentence examples for methods for equation from inspiring English sources

This lack of smoothness of the solution near (t=0) results in a decay in the order of the practical performance of familiar timestepping methods for equation (1).

Numerical oscillation of θ-methods and Runge-Kutta methods for equation (x'(t)+ax t)+a_{1}x([t-1])=0) was investigated in [20, 21], respectively.

In this paper, we will investigate the numerical properties, including the stability and oscillation of Runge-Kutta methods for equation x ′ ( t ) = a x ( t ) + a 0 x ( M [ t + N M ] ).

In Table 1, we list the mean-square errors of the IM and Milstein methods for equation (5.1) with the parameter I. Table 1 shows that the computational accuracy of the IM method is, to some degree, improved even for the non-stiff problem.

On the other hand machine learning methods for equation discovery [ 8] are applicable in complex modeling scenarios [ 9].

Some applications and numerical methods for equations where the operator acting on the delay term is bounded have been studied in [5 8].

The next result is the monotone method for equation (11) using coupled lower and upper solutions.

In this subsection, we will present the semi-discrete LDG method for equation (1.1).

In this section, we will perform the Lie group method for Equation (1).

Figure 3 depicts the mean-square error behavior of the IM method for equation (5.1) with the parameter II.

The proposed method is applied to some special examples in order to illustrate the applicability and accuracy of the proposed method for equation (1.1).