The phrase "uncertain spaces" is grammatically correct and can be used in written English.
It can be used to describe places, situations, or concepts that are ambiguous, unpredictable, or difficult to define. Example: The artist's abstract paintings explore the uncertain spaces between reality and imagination, blurring the lines between what is real and what is perceived.
The uncertain space between prose and poetry, between self-love and love of another – all this brings me back to this rather lonely summer and to questions I still don't have the answers to.
To handle this distance computation issue in uncertain space, Voronoi diagrams have been introduced by [46, 47].
The proposed approach provides information about variation of the optimal objective and optimal design configuration over the entire uncertain space.
Ghezavati and Saidi-Mehrabad (2010) proposed a mathematical model for the CM problem integrated with group scheduling in an uncertain space.
Uniformly distributed samples are generated for truncated random variables in the supported intervals and design variables in the specified intervals to approximate cover the entire uncertain space fully.
Cumulative probability curves are constructed to assess the financial risk related to uncertain space for different standard deviations of expected mean values.
The approach is based on the idea of High Dimensional Model Representation technique which utilize a reduced number of model runs to build an uncertainty propagation model that expresses the variability of optimal solution in the uncertain space.
Usually, due to computational requirement, the number of flow simulations that can be carried out is limited, which (1) leads to an under sampling of the uncertain space and (2) does not allow to capture curvatures and non-linearities in the reservoir flow behavior in face of uncertain parameters.
In order to avoid repeated function evaluations and improve computational efficiency, a surrogate model is established using back-propagation (BP) neural networks which can approximate the relationships between the inputs and system responses properly in almost entire uncertain space using the proposed given available data.
