The phrase "adaptive computation" is correct and usable in written English.
It can be used in contexts related to computer science, artificial intelligence, or any field that involves systems that adjust their processing methods based on varying conditions or inputs.
Example: "The research focuses on adaptive computation techniques that allow algorithms to optimize their performance in real-time."
Alternatives: "dynamic computation" or "flexible processing".
The emphasis is on comparison of the performance of adaptive RKDG method using different indicators, with an objective of obtaining efficient and reliable indicators to obtain better performance for adaptive computation to save computational cost.
According to the autonomic computing white paper [2], the term "adaptive computation" (AC) is used to denote any computing activity that implements either the complete autonomic control loop, or just one or more functions in the loop, i.e., monitor, analyze, plan, or execute.
We leverage the well-accepted autonomic control loop proposed in IBM's autonomic computing white paper [2] to be the basic model of adaptive computation.
The adaptive computation in the flow problem is permitted by two different basis functions.
Fields as diverse as fluid dynamics, materials science, biomechanics, and ecology make use of parallel adaptive computation.
MGDS utilizes an adaptive computation distribution mechanism to efficiently allocate computation of the employed ±1 FS array to blocks or frames of video sequences.
With the increasing use of autonomic computing technologies, JEE servers are implemented with more and more adaptive computations.
As an added dimension, we also describe how the proposed approach enables the design of both time continuous/discontinuous high-order A-Stable schemes of order 2q, L-Stable schemes of order 2q−1 and 2q−2, and subsequently provide the requisite hierarchical computational structure of the resulting equations that is useful for time adaptive computations.
These adaptive computations implement a relative value code that may explain the characteristic context-dependent nature of behavioral violations of classical normative theory.
They are also a way to explore and exploit an original formalism of asynchronous, distributed and adaptive computations with such precious properties as self-organization, emergence, robustness and more generally abilities to cope with an intelligent interaction with the world.
Data-driven, adaptive computations are key to enabling the deployment of accurate and efficient stream mining systems, which invoke suitably configured queries in real-time on streams of input data.
