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This network is able of estimating approximate solution of assumed equation using the learning algorithm which is based on steepest descent rule.
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Assume Equation (6.4) is super-linear, that is p > 1.
Here we assume Equation (1) is of index-1 which implies μ = 1 in Equation (2).
Before performing the numerical methods, we assume Equation (2.3) has a unique and sufficiently smooth solution.
If we assume equation (4.9) with 1 + λ J Δ t ≠ 0, then θ is asymptotically stable (see Remark 4.2).
That is, assuming Equation (1) and that f(x) ∼ 𝒢𝒫(m, k), it follows that, (2) where l(x i, x j )= k(x i, x j )+σ δ(x i, x j ).
Similarly, the covariance between g1 and g2 conditional on T2 is: Furthermore, assuming equation (5), the above results can be combined to show that conditional on T2, g has a multivariate normal distribution with null mean and covariance matrix: 11 where.
The solitary wave ansatz in terms of cosh p is assumed as Equation 25 (see[7, 10, 11]).
We assumed that equation (32) has at least one positive root denoted as (omega_{02}).
The solitary wave ansatz in terms of tanh p is assumed as Equation 26 (see[7, 10]).
If constant exposure is assumed in equation 3, TTG can be directly derived from the individual patient parameter estimates: TTG = [log KkillExposure) − log Kgrow)]/λ [ 27].
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com