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The results predicted with the neural network have been verified by solving the fundamental equations and by comparison with experimental data.
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That is because each is unique, and has to be verified by solving the cryptographic problem to be transferred.
The accuracy and efficiency of the method is verified by solving some examples of physical interest.
The effectiveness of the proposed method is verified by solving a missile benchmark problem.
This new technique is verified by solving shear and uniaxial elongational flows.
Models which have been described quantitatively using mathematical equations can be verified by solving the equations in a simulator.
Finally, the effectiveness of this method is verified by solving multi-objective optimization problems of helical gear transmission.
The result of the proof is verified by solving a finite approximation of PHI with three existing codes, two of which are not constrained by these assumptions.
These results obtained from perturbation analysis are verified by solving the temporal equation of motion using fourth-order Runge Kutta method.
A parameter fitting procedure based on experimental data for single crystalline response, taken from the literature, is detailed and the material model as well as the algorithmic scheme are verified by solving representative boundary value problems.
And it has been verified by Jim Romenesko.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com