Forward and backward uncertainty quantification with active subspaces: application to hypersonic flows around a cylinder
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Publication:2122694
DOI10.1016/j.jcp.2019.109079OpenAlexW2785713819WikidataQ126770122 ScholiaQ126770122MaRDI QIDQ2122694
Andrea F. Cortesi, Pietro Marco Congedo, Paul G. Constantine, Thierry E. Magin
Publication date: 7 April 2022
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://hal.inria.fr/hal-03052824/file/manuscript_CAF.pdf
inverse problemshypersonic flowsBayesian inferenceuncertainty quantificationsurrogate modelingactive subspaces
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Uses Software
Cites Work
- Global sensitivity analysis for DSMC simulations of hypersonic shocks
- Projection pursuit
- Towards the ultimate conservative difference scheme. IV: A new approach to numerical convection
- Design and analysis of computer experiments. With comments and a rejoinder by the authors
- Statistical and computational inverse problems.
- Regression analysis under link violation
- Gradient-based methods for uncertainty quantification in hypersonic flows
- Towards the ultimate conservative difference scheme. V. A second-order sequel to Godunov's method
- An unstructured, three-dimensional, shock-fitting solver for hypersonic flows
- Exploiting active subspaces to quantify uncertainty in the numerical simulation of the hyshot II scramjet
- Dimension-independent likelihood-informed MCMC
- Adaptive surrogate modeling by ANOVA and sparse polynomial dimensional decomposition for global sensitivity analysis in fluid simulation
- Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction
- Accelerating Markov Chain Monte Carlo with Active Subspaces
- Likelihood-informed dimension reduction for nonlinear inverse problems
- Active Subspaces
- A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code
- Evaluating Derivatives
- Handbook of Markov Chain Monte Carlo
- Upwind Difference Schemes for Hyperbolic Systems of Conservation Laws
- Introduction to Bayesian Scientific Computing
- Metamodels for computer-based engineering design: Survey and recommendations
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