Sentence examples for models for problems from inspiring English sources

This feature allows a more flexible design of discrete models for problems with complicated geometries, shocks, or singular points.

This paper reviews recent advances in the area of surrogate models for problems in modeling, feasibility analysis, and optimization.

In this context, machine learning algorithms, able to seek models for problems with multi-source data, have to be used.

Numerical results reveal that this adaptive sampling approach is very promising for constructing accurate Kriging models for problems with diverse characteristics.

However, some studies have shown that even multivariate EDAs fail to build accurate models for problems in which there is an intrinsic pairwise independence between variables.

MapReduce [1] is considered one of the most prominent programming models for problems of large scale data processing of which the cluster analysis.

One of the most common psychological treatments is cognitive-behavioural therapy (CBT), combining the cognitive and behavioural models for problem gambling.

Introduces probabilistic modeling for problems of inference and machine learning from data, emphasizing analytical and computational aspects.

Clearly, MapReduce is the most prominent model for problems of large-scale data processing.

The gradient system represents the steady-state case of reaction-diffusion system which is a model for problems arising from biology, chemistry, physics and ecology, etc.

The equilibrium problems, in its various forms, found application in optimization problems, fixed point problems, and convex minimization problems; in other words, equilibrium problems are a unified model for problems arising in physics, engineering, economics, and so on (see [26 29]).