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We elaborated on the foundations of our numerical method and validated it on assorted test cases.
The model was solved numerically by using finite difference method and validated against experimental data.
The present model is solved with the finite element method and validated with experimental and numerical results from literature.
In Study 1, a complete list of factors was generated using a focus group method and validated on a broader sample using a survey method with network professionals.
The proposed model is solved numerically based on a finite volume method and validated with experimental data.
The in silico model was built by the multiple linear regression method and validated by the residual analysis, the normal Probability Probability plot and Williams plot.
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Further research is needed to fully explore the potential applications of the proposed method and validate it.
The paper illustrates the method and validates the method through analytical analysis and through simulation of real and synthetic data.
Therefore, it needs to perform the calculations based on the Gibbs free energy minimization method and validate the data through experimental means.
Finally, a real-world case study in Taiwan was used to explain the research process of the GQFD method and validate the practicality of the proposed method.
We propose a nonlinear elastic impedance inversion method based on a fast Markov chain Monte Carlo (MCMC) method, and validate the feasibility and robustness of the method by testing the noise immunity of well data.
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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.
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