The phrase "adapting the underlying" is correct and usable in written English.
It can be used when discussing modifications or changes made to foundational elements or principles in a particular context.
Example: "The researchers are focused on adapting the underlying algorithms to improve the system's performance."
Alternatives: "modifying the core" or "altering the fundamental".
For coping with such circumstances, adaptive knowledge modeling is tasked with adapting the underlying knowledge model according to the environment.
Our implementations exploit the hardware parallelism of both types of architectures by carefully adapting the underlying numerical computations.
By demonstrating the architecture of the autoscaling method and providing performance measurements gathered in an OpenStack cloud environment, we show how the injection of aspect-oriented code into cloud applications allows for improving application performance by automatically adapting the underlying virtual environment to pre-defined performance goals.
That meant adapting the underlying principal of the digital world, where a semiconductor circuit is either open or closed, on or off, to create sharp images.
This second "surrogate" layer monitors the original AI for bias, alerting the developer if something is amiss or, even better, adapting the underlying model on the fly to remove bias.
Taxonomical knowledge represented in an ontology is explicit and accessible to domain experts, while algorithms which perform hierarchical classification often act as black boxes, and amending the classification methodology requires adapting the underlying software or re-training a complex statistical model.
These architectures use an online algorithm to make run-time reconfiguration decisions that continually adapt the underlying architecture to match the application's current computational demand.
We have also adapted the underlying statistical model by proper training with typical real astronomical images (from Kitt Peak observatory) with different blurs and noise levels; as we expected in this case, results are clearly better than the previous ones (in a range between 1 and 3 dBs).
Code written with OpenCL can run on a wide variety of platforms, adapting to the underlying architecture.
The pontics had an ovate design and were adapted to the underlying mucosa with tight but noncompressive contact.
Furthermore, a new dynamic bin width allocation method is proposed to estimate sample conditional error and this method adapts to the underlying probabilities.
