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For chemically reacting flow simulations, existing splitting methods often generate nonphysical waves at stiff reaction fronts.
While repair can be enhanced through surgical intervention, current methods often generate inferior fibrocartilage and repair is transient.
However, different experimental methods often generate different levels of information.
Other methods often generate a mixture of products due to multiple insertions.
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Even so, we have to interpret this result with caution because it has been reported that this method often generates false positives under certain conditions [ 31, 32].
Many of the catalysts are of high value, and the destructive separation methods currently employed often generate substantial amounts of waste.
Because of the nonlocal property of fractional differential operators, the numerical methods for SFDEs often generate dense or even full coefficient matrices.
Gene signatures of many gene-based classification methods are often generated by genes selected independently, even though their functional products may interact with each other, and the selected gene markers may contain redundant information.
In such cases, the existing reconstruction algorithms, which are based on the least-squares criteria (i.e., the regularized Newton method) [ 9, 10], often generate distorted images with severe artifacts.
Because of the nonlocal property of fractional differential operators, the numerical methods for fractional diffusion equations often generate dense or even full coefficient matrices.
Numerical methods for space-fractional diffusion equations often generate dense or even full stiffness matrices.
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