Studies on performance impact due to the high-dimensional geometric uncertainties face the
“curse of dimensionality” problem, making the cost of uncertainty quantification (UQ) …

The objective of this work is to propose a data-driven Bayesian inference framework to
efficiently identify parameters and select models of nonlinear aeroelastic systems. The …

View Video Presentation: https://doi. org/10.2514/6.2022-1100. vid Uncertainty
Quantification (UQ) plays a critical role in engineering analysis and design. Regression is …

View Video Presentation: https://doi. org/10.2514/6.2023-1183. vid This work focuses on
evaluating machine learning methods and their applicability to the generation of an …

Surrogate modeling is a viable solution for applications involving repetitive evaluations of
expensive computational fluid dynamics models, such as uncertainty quantification and …

View Video Presentation: https://doi. org/10.2514/6.2023-1184. vid In this paper we propose
a new approach for nonlinear regression and uncertainty quantification. The method is …

Computational fluid dynamics (CFD) plays a critical role in many scientific and engineering
applications, with aerodynamic design optimization being a primary area of interest …

Estimation of inflow conditions, such as wind speed and angle of attack, is vital for assessing
aerodynamic performance of a lifting profile. This task is particularly challenging in the field …

We propose a spatial-temporal multi-fidelity Gaussian process regression framework for the
fusion of flow field data with various availabilities and fidelities but not sufficiently large to …

Abstract Machine learning models and algorithms have been employed in various
applications, from prognostic scrutinizing, learning and revealing patterns in data …