Numerical methods for uncertainty quantification and bayesian update in aerodynamics

This process is experimental and the keywords may be updated as the learning algorithm improves. This is a preview of subscription content, log in to check access. Brand, M. Linear Algebra Appl. Bungartz, H. Acta Numer.

Chen, Q. Gerhold, T. Golub, G. Johns Hopkins Studies in the Mathematical Sciences. Griebel, M. In: Foundations of Computational Mathematics, Santander London Math. Lecture Note Ser. Cambridge Univ. Hida, T. Mathematics and its Applications, vol. Hosder, S. Khoromskij, B. Lanczos, C. Recent advances in numerical aerodynamics Report of the German research initiative MUNA which aimed to develop methods and procedures for reducing various types of uncertainties present in numerical flow simulations Written by experts in the field working in the academic or in the industrial sector.

Front Matter Pages Pages Improved Mesh Deformation. Wolf, D. Henes, S. The underlying theme of this subject is uncertainty quantification, with a heavy dose of computational statistics. Part one of the subject focuses on uncertainty propagation and assessment, with foundations in Monte Carlo simulation and in approximation theory: Monte Carlo methods; variance reduction; global sensitivity analysis; polynomial approximation; Gaussian process regression and scattered data approximation; stochastic Galerkin and collocation methods; sparse grids, tensor decompositions, and other methods for high-dimensional approximation and integration.

Part two of the subject focuses on the interaction of models with observational data, from a largely Bayesian statistical perspective: Bayesian modeling and inference; inverse problems; Markov chain Monte Carlo methods; sequential Monte Carlo methods; nonlinear filtering and data assimilation; model selection; model criticism and validation.

Introduction to probability and statistics, with applications to aerospace engineering.