Drawing on his teaching notes for a graduate course on computational methods and
uncertainty quantification for inverse problems, Bardsley presents a self-contained introduction to computational inverse problems for a wide range of students, and proceeds step-by-step to Markov chain Monte Carlo sampling methods.
UNCERTAINTY QUANTIFICATION OF THE CROSSTALK EFFECT IN A TRANSMISSION LINE NETWORK
Wang et al., "Spectral
uncertainty quantification, propagation and optimization of a detailed kinetic model for ethylene combustion," Proceedings of the Combustion Institute, vol.
Daniel, "Calculation of generalized polynomial-chaos basis functions and gauss quadrature rules in hierarchical
uncertainty quantification," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol.
Weirs, "Verification, validation and
uncertainty quantification workflow in CASL," Sandia National Laboratories, no.
Steffen, "
Uncertainty quantification techniques applied to rotating systems: A comparative study," Journal of Vibration and Control, vol.
Since uncertainty always exists in both aerodynamics part and structure part when a theoretical method is applied to the aeroelastic stability analysis, aeroelastic
uncertainty quantification (AUQ) is somewhat unavoidable in the theoretical analysis [5].
The statistical approach toward inverse problems from a Bayesian point of view has been very important for the development of
uncertainty quantification. Textbooks such as [5-7] provide an extensive literature on the Bayesian statistical aspects in inverse problems.