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Our approach is a quantitative solution that offers a more objective approach than subjective, semi-quantitative methods.
The ability to design a quantitative solution, yielding precise dimensions of the filling channels at all points, is a key responsibility, perhaps the key responsibility, of the casting engineer.
We address the open question of how to determine the strength of the inductive bias of programmed weights; we present a quantitative solution which takes the network architecture, the prior knowledge, and the training data into consideration.
Together with inter-scale model formulations, a zoom-in/zoom-out process is carried out between the scales, by trial-and-error and through reasoning, to arrive at a global formulation of a quantitative solution, in order to derive, eventually, the general from the particular.
However, the solutions can be manipulated to provide such a quantitative solution.
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Thus, this work cannot be considered a definitive quantitative solution.
The search for a comprehensive quantitative solution that can cope with all the challenges continues.
Unlike maximum likelihood integration (MLI), the Bayesian structure inference approach provides a systematic and quantitative solution to these kinds of problems.
As an example of the quantitative solution of the basic set of equations, a simple reaction in a volatile liquid is considered; the results obtained show considerable acceleration of the diffusion-controlled reaction due to convective transport in an unsaturated porous medium.
However, the quantitative solution of potential distribution in such a system, which would be necessary for a test of experimental data, has not been made.
The classic, quantitative solution originates with Hosiasson-Lindenbaum (1940).
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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