Normalizing Flow Ensembles for Rich Aleatoric and Epistemic Uncertainty Modeling

Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty

A proper representation of the uncertainty involved in a prediction is an important prerequisite for the acceptance of machine learning and decision support technology in safety-critical application domains such as medical diagnosis. Despite the existence of various probabilistic approaches in these fields, there is arguably no method that is able to distinguish between two very different sourc...

Epistemic uncertainty quantification for RANS modeling of the flow over a wavy wall

While Reynolds-averaged Navier-Stokes (RANS) simulations remain the most affordable technique for simulating complex turbulent flows, the inability of linear eddy viscosity models to correctly predict flow separation and reattachment limits the reliability of the simulation outcome. A methodology to quantify the uncertainty in the simulation outcome related to the form of the turbulence model u...

Epistemic uncertainty quantification of RANS modeling for an underexpanded jet in a supersonic cross flow

Modeling of Epistemic Uncertainty in Reliability Analysis of Structures Using a Robust Genetic Algorithm

In this paper the fuzzy structural reliability index was determined through modeling epistemic uncertainty arising from ambiguity in statistical parameters of random variables. The First Order Reliability Method (FORM) has been used and a robust genetic algorithm in the alpha level optimization method has been proposed for the determination of the fuzzy reliability index. The sensitivity level ...

Robustness-based portfolio optimization under epistemic uncertainty

In this paper, we propose formulations and algorithms for robust portfolio optimization under both aleatory uncertainty (i.e., natural variability) and epistemic uncertainty (i.e., imprecise probabilistic information) arising from interval data. Epistemic uncertainty is represented using two approaches: (1) moment bounding approach and (2) likelihood-based approach. This paper first proposes a ...