"modeling tasks" is a correct and usable phrase in written English
It can be used to refer to tasks or assignments related to creating or developing models, often in the fields of science, engineering, or mathematics. Example: The students were given a variety of modeling tasks to complete in their physics class, including building and testing a model of a simple machine.
In addition to designing self-describing Earth system models, the regular modeling tasks are also simplified and automated by using a scientific workflow which provides meaningful abstractions for the model, computing environment and provenance/metadata collection mechanisms.
We test this model through a free simulation experiment of collaborative process modelers structured around a set of modeling tasks.
For example, in the kinematics unit, when students modeled a single agent, the computational modeling tasks were presented in the order of increasing complexity, starting from constant shapes (squares to triangles to circles) to spirals of the same shapes (where speed became a function of the acceleration) to modeling real-world systems involving constant and variable speed segments.
A number of methodologies and technologies required in order to achieve the long-term goal of cooperation of heterogeneous autonomous robots are discussed, including modeling tasks and robot abilities, task assignment and redistribution, robot behavior modeling and programming, robot middleware and robot simulation.
By combining these modeling tasks into workflows, as in Figure 1, one can accomplish the modeling objectives listed above.
We employ the MEF to map conceptual modeling tasks to different levels of cognitive complexity and classify current modeler expertise levels.
The automated QSAR modeling framework is not a black-box prediction system, rather it is an extensible and highly customizable tool to develop the robust predictive models and provide the output of all modeling task for the diverse application and reproduction of historical predictions.
For a particular modeling task, the user specifies an incomplete model that includes a set of entities in the observed system and constraints on the possible interactions between them.
This paper compares the performance of three predictive models all developed for the same, well-defined modeling task.
First, a clear definition of a modeling task is given, followed by reviewing the theoretical modeling capabilities of NN and NN model estimation.
This points to general problems for data-rich scenarios such as the ones involving wearable sensors in real-world settings: What is the right amount of information about individual interactions that is appropriate for a given modeling task, so that the relevant information is retained but the model stays as parsimonious as possible [ 44]?
