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The performances of kriging, stochastic simulations and sequential self-calibration inversion are assessed when characterizing a non-multiGaussian synthetic 2D braided channel aquifer. The comparison is based on a series of criteria such as the reproductio ...
The performances of kriging, stochastic simulations and sequential self-calibration inversion are assessed when characterizing a non-multiGaussian synthetic 2D braided channel aquifer. The comparison is based on a series of criteria such as the reproductio ...
Genetic programming is used in this paper for modeling the fatigue life of several fiber-reinforced composite material systems. It is shown that if the genetic programming tool is adequately trained, it can produce theoretical predictions that compare favo ...
In this work we present the concept of Uncertainty Feature Optimization (UFO), an optimization framework to handle problems due to noisy data. We show that UFO is an extension of standard methods as robust optimization and stochastic optimization and we sh ...
The application of decentralized reputation systems is a promising approach to ensure cooperation and fairness, as well as to address random failures and malicious attacks in Mobile Ad-Hoc Networks. However, they are potentially vulnerable to liars. With o ...
While most current building simulation tools consider occupants as predictable robots the true nature of human behaviour is more complex. This article describes a set of stochastic models aimed at capturing this complexity by decoupling occupant presence f ...
A continuous time process of alternating stochastic growths and deterministic decays is proposed as a simple model for the interannual dynamics of snow water equivalent (SWE) storage and melting. The related stationary properties are studied, and an integr ...
We study adaptive route choice models that explicitly capture travelers' route choice adjustments according to information on realized network conditions in stochastic time-dependent networks. Two types of adaptive route choice models are explored: an adap ...
In this work we present the concept of Uncertainty Feature Optimization (UFO), an optimization framework to handle problems due to noisy data. We show that UFO is an extension of standard methods as robust optimization and stochastic optimization and we sh ...