In stochastic optimization models, the optimal solution heavily depends on the selected probability model for the scenarios. However, the scenario models are typically chosen on the basis of statistical estimates and are therefore subject to model error. W ...
Both marginal and dependence features must be described when modelling the extremes of a stationary time series. There are standard approaches to marginal modelling, but long-and short-range dependence of extremes may both appear. In applications, an assum ...
This thesis is devoted to the derivation of error estimates for partial differential equations with random input data, with a focus on a posteriori error estimates which are the basis for adaptive strategies. Such procedures aim at obtaining an approximati ...
This paper proposes randomly-generated synthetic time series incorporating climate change forecasts to quantify the variation in energy simulation due to weather inputs, i.e., Monte Carlo analysis for uncertainty and sensitivity quantification. The method ...
This paper presents a methodology for reducing the uncertainty related to the structural behaviour of an existing building in view of a vulnerability assessment regarding future earthquake actions. Estimating the lateral load resistance is a step towards e ...
