The phrase "allow for generating" is correct and usable in written English.
It can be used when discussing the capability or permission to create or produce something, often in a technical or procedural context.
Example: "The new software update will allow for generating detailed reports with just a few clicks."
Alternatives: "enable the creation of" or "permit the generation of".
These tricks have led to findings that allow for generating three-dimensional microstructures, using graphene as a more efficient delivery channel, and creating a new lens that would produce intense ultrasonic energy.
Quantum-optical techniques allow for generating controllable spin-spin interactions between ions, making trapped ions an ideal quantum simulator of Heisenberg chains.
The fundamental requirement for such methods are that they should allow for generating basis numbers with low intra-sample variance (the values produced for the same device) simultaneously with high inter-sample variance (the values produced for different devices).
Future development plans include the ability to expand metadata options and allow for generating metadata files that comply with different standards for various disciplines.
Continual progress in next-generation sequencing allows for generating increasingly large metagenomes and studying multiple metagenomes over time or space.
A Pareto-optimal state observer allows for generating residual signals, which can be utilised for fault detection.
Satellite multi-temporal data allows for generating cloud-free, radiometrically and phenologically consistent pixel based image composites of regional scale.
Creation of procedural 3D building models can significantly reduce the costs of modeling, since it allows for generating a variety of similar shapes from one procedural description.
This non-explosive test allows for generating consistent and repeatable wide area pressure pulse loads to facilitate performance comparison and optimization of armor panel designs.
Because a Monte Carlo-based uncertainty analysis is computationally very demanding, especially when complex models are involved, we developed a fast indicative uncertainty assessment method that allows for generating proxies of the Monte Carlo-based result in terms of the mean prediction and its associated uncertainty based on a single model evaluation.
Namely, based on the mini-size training set, the proposed method can also obtain a better result; thus, this model allows for generating ideal strong classifiers without the need for large volumes of training samples.
