… reviews the generic steps of stochastic optimizations in renewable energy applications, from
the modelling of the uncertainties … Furthermore, the benefits and drawbacks of the stochastic …

… s and a training data set \(\mathcal {D}\), the training of a neural network is a stochastic
process and therefore the resulting neural network \(f_\theta\) is based on a random variable, …

… simulate a random or stochastic simulation model. If the computational model is available,
the stochastic simulation … times, changing only the input random variable that is randomly …

… and structural uncertainties in turbulence models. We review recent literature on data-free
(uncertainty … It is also referred to as stochastic process when the index is time coordinate t. …

… accurate and efficient modelling of these uncertainties is … uncertainty modelling techniques
is reviewed, discussed, and categorised into the following: probabilistic techniques, stochastic …

… in this review include stochastic process, probabilistic simulation and uncertainty modelling.
… probability distributions has been thoroughly reviewed in this paper. Interactions, …

… This study reviews recent advances in UQ methods used in deep learning, investigates
the application of these methods in reinforcement learning, and highlights the fundamental …

… of the field variable under the rather strict assumption of statistical homogeneity of the random
field. … This section provides a concise review of the basic interval arithmetical operations. …

… process of the literature review, the author of this paper found that many scholars considered
a single uncertainty in … , we found that random uncertainty theory is used to study numerous …

… reviews the canonical models of these communities, and proposes a universal modeling …
Let W 1 , W 2 , … , W t , … represent a stochastic process that might describe stock prices, …