Search Results for author:
Found 9 papers, 4 papers with code
Conformal Approach To Gaussian Process Surrogate Evaluation With Coverage Guarantees
Gaussian processes (GPs) are a Bayesian machine learning approach widely used to construct surrogate models for the uncertainty quantification of computer simulation codes in industrial applications.
Hoeffding decomposition of black-box models with dependent inputs
The elements of this decomposition can be expressed using oblique projections and allow for novel interpretability indices for evaluation and variance decomposition purposes.
Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review
Data Assimilation (DA) and Uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics.
Quantile-constrained Wasserstein projections for robust interpretability of numerical and machine learning models
Numerical experiments on real case studies, from the UQ and ML fields, highlight the computational feasibility of such studies and provide local and global insights on the robustness of black-box models to input perturbations.
Sample selection from a given dataset to validate machine learning models
The selection of a validation basis from a full dataset is often required in industrial use of supervised machine learning algorithm.
Developments and applications of Shapley effects to reliability-oriented sensitivity analysis with correlated inputs
This paper proposes new target sensitivity indices, based on the Shapley values and called "target Shapley effects", allowing for interpretable sensitivity measures under dependent inputs.
Statistics Theory Applications Methodology Statistics Theory
An information geometry approach for robustness analysis in uncertainty quantification of computer codes
Thus, a practical robustness analysis methodology should rely on a coherent definition of a distribution perturbation.
Statistics Theory Statistics Theory
Poincaré inequalities on intervals -- application to sensitivity analysis
We give semi-analytical results for some frequent distributions (truncated exponential, triangular, truncated normal), and present a numerical method in the general case.
Open TURNS: An industrial software for uncertainty quantification in simulation
EDF R&D, Airbus Group and Phimeca Engineering started a collaboration at the beginning of 2005, joined by IMACS in 2014, for the development of an Open Source software platform dedicated to uncertainty propagation by probabilistic methods, named OpenTURNS for Open source Treatment of Uncertainty, Risk 'N Statistics.
Computation Statistics Theory Statistics Theory
