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In the common case where one expects an equal distribution of observations among the two groups (E1 = E2 = n/2), this maximum possible value is equal to n (see Methods), making equation (1) correct.
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More sophisticated prediction methods make use of weakly nonlinear theories, the governing equations being solved by a PSE (Parabolized Stability Equations) approach.
The neural networks are frequently used to model complicated problems which are difficult to make equations by analytical methods.
Prediction methods make mistakes.
This method makes use of an equation for the evolution with time of the volume fraction crystallized.
This method makes it possible to obtain model equations with smooth functional coefficients, describing the effect of the microstructure size.
One method makes use of a set of differential-algebraic equations (DAE in short).
To solve the optimization in Equation (3), we use the Limited memory Broyden, Fletcher, Goldfarb and Shanno quasi-Newton method made available by Byrd et al. (1994).
In Section 2, we describe and further develop Zhang and Peng's modification of the Fan sub-equation method by making good use of the extended solutions of the general elliptic equation.
A method is presented by making the equilibrium equations of motion into a subspace equation spanned in terms of the columns of a projection basis obtained by considering the use of the contribution of the lower modes and the first two terms of the Neumann expansion of the contribution of the unavailable modes.
A comparison of Runge Kutta and orthogonal collocation methods is made for the solution of initial value ordinary differential equations.
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