A View on Model Misspecification in Uncertainty Quantification

Estimating uncertainty of machine learning models is essential to assess the quality of the predictions that these models provide. However, there are several factors that influence the quality of uncertainty estimates, one of which is the amount of model misspecification. Model misspecification always exists as models are mere simplifications...

Generation of design allowables on the open hole tensile strength of composite laminates using sensitivity analysis and uncertainty quantification framework

Aeronautical industries are concerned about the cost effective generation of design allowables for composite laminates. Design allowables take into account the variabilities arising from different sources (material, manufacturing, defects etc.,) which are determined using expensive and time consuming experimental campaigns....

On the investigation of utility functions on optimal sensor locations

Structural Health Monitoring uses data collected from sensors placed on structures to determine their operating condition and whether maintenance is required. Often, optimal sensor placement strategies are used to find the optimal locations for the identification of their modal properties, structural parameters and/or abnormal behaviours under...

Quantifying Accuracy and Uncertainty in Data-Driven Flight Trajectory Predictions with Gaussian Process Regression

Several initiatives are developed to shift the current paradigm in Air Traffic Management from the tactical-based approach to more strategic-based coordination of flights. This transformation of the ATM system relies on the improvement of predictive models for the 4D flight trajectories. A variety of performance-based and data-driven approaches...

Consistency and Finite Sample Behavior of Binary Class Probability Estimation

We investigate to which extent one can recover class probabilities within the empirical risk minimization (ERM) paradigm. We extend existing results and emphasize the tight relations between empirical risk minimization and class probability estimation. Following previous literature on excess risk bounds and proper scoring rules, we derive a...

Uncertainty quantification in steady state simulations of a molten salt system using polynomial chaos expansion analysis

Uncertainty Quantification (UQ) of numerical simulations is highly relevant in the study and design of complex systems. Among the various approaches available, Polynomial Chaos Expansion (PCE) analysis has recently attracted great interest. It belongs to non-intrusive spectral projection methods and consists of constructing system responses...

On the Generic Utilization of Probabilistic Methods for Quantification of Uncertainty in Process-based Morhpodynamic Model Applications

A variety of uncertainty sources are inherent in process-based morphodynamic modelling applications. There is an increasing demand for the quantification of these uncertainties. This contribution introduces a probabilistic-morphodynamic (PM) modelling framework that enables this quantification. The PM modelling framework provides a systematic...

Efficient uncertainty quantification in unsteady aeroelastic simulations

An efficient uncertainty quantification method for unsteady problems is presented in order to achieve a constant accuracy in time for a constant number of samples. The approach is applied to the aeroelastic problems of a transonic airfoil flutter system and the AGARD 445.6 wing benchmark with uncertainties in the flow and the structure.

A robust and efficient uncertainty quantification method for coupled fluid-structure interaction problems

A robust and efficient uncertainty quantification method is presented for resolving the effect of uncertainty on the behavior of multi-physics systems. The extrema diminishing method in probability space maintains a bounded error due to the interpolation of deterministic samples at constant phase in a transonic airfoil flutter problem.

An unsteady adaptive stochastic finite elements uncertainty quantification method for fluid-structure interaction problems

A Monomial Chaos Approach for Efficient Uncertainty Quantification in Computational Fluid Dynamics

A monomial chaos approach is proposed for efficient uncertainty quantification in nonlinear computational problems. Propagating uncertainty through nonlinear equations can still be computationally intensive for existing uncertainty quantification methods. It usually results in a set of nonlinear equations which can be coupled. The proposed...

Efficient uncertainty quantification using a two-step approach with chaos collocation

In this paper a Two Step approach with Chaos Collocation for efficient uncertainty quantification in computational fluid-structure interactions is followed. In Step I, a Sensitivity Analysis is used to efficiently narrow the problem down from multiple uncertain parameters to one parameter which has the largest influence on the solution. In Step...

A Monomial Chaos Approach for Efficient Uncertainty Quantification in Computational Fluid Dynamics

A monomial chaos approach is proposed for efficient uncertainty quantification in nonlinear computational problems. Propagating uncertainty through nonlinear equations can still be computationally intensive for existing uncertainty quantification methods. It usually results in a set of nonlinear equations which can be coupled. The proposed...

Reliable Computational Predictions by Modeling Uncertainties Using Arbitrary Polynomial Chaos

Inherent physical uncertainties can have a significant influence on computational predictions. It is therefore important to take physical uncertainties into account to obtain more reliable computational predictions. The Galerkin polynomial chaos method is a commonly applied uncertainty quantification method. However, the polynomial chaos...

Reliable Computational Predictions by Modeling Uncertainties Using Arbitrary Polynomial Chaos

Inherent physical uncertainties can have a significant influence on computational predictions. It is therefore important to take physical uncertainties into account to obtain more reliable computational predictions. The Galerkin polynomial chaos method is a commonly applied uncertainty quantification method. However, the polynomial chaos...

Efficient uncertainty quantification using a two-step approach with chaos collocation

In this paper a Two Step approach with Chaos Collocation for efficient uncertainty quantification in computational fluid-structure interactions is followed. In Step I, a Sensitivity Analysis is used to efficiently narrow the problem down from multiple uncertain parameters to one parameter which has the largest influence on the solution. In Step...

A posteriori error analysis for stochastic finite element solutions of fluid flows with parametric uncertainties

Accounting for uncertainty in numerical simulations is a growing concern and a great deal of methods have recently been developed, such as the Polynomial Chaos which basically consists in a spectral approximation of the surface response of the solution by stochastic finite elements. However, criteria for refinement of the spectral space have so...