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Found 11 papers, 0 papers with code
Learning non-stationary and discontinuous functions using clustering, classification and Gaussian process modelling
A crucial aspect in surrogate modelling is the assumption of smoothness and regularity of the model to approximate.
Multi-objective robust optimization using adaptive surrogate models for problems with mixed continuous-categorical parameters
The optimization problem is formulated by considering quantiles of the objective functions which allows for the combination of both optimality and robustness in a single metric.
Rare event estimation using stochastic spectral embedding
Estimating the probability of rare failure events is an essential step in the reliability assessment of engineering systems.
Active learning for structural reliability: survey, general framework and benchmark
Using this framework, we devise 39 strategies for the solution of $20$ reliability benchmark problems.
Emulation of stochastic simulators using generalized lambda models
Stochastic simulators are ubiquitous in many fields of applied sciences and engineering.
Epidemiology
Computation
Methodology
Global sensitivity analysis for stochastic simulators based on generalized lambda surrogate models
Due to this random nature, conventional Sobol' indices, used in global sensitivity analysis, can be extended to stochastic simulators in different ways.
Stochastic spectral embedding
Constructing approximations that can accurately mimic the behavior of complex models at reduced computational costs is an important aspect of uncertainty quantification.
Replication-based emulation of the response distribution of stochastic simulators using generalized lambda distributions
Due to limited computational power, performing uncertainty quantification analyses with complex computational models can be a challenging task.
Extending classical surrogate modelling to high-dimensions through supervised dimensionality reduction: a data-driven approach
Thanks to their versatility, ease of deployment and high-performance, surrogate models have become staple tools in the arsenal of uncertainty quantification (UQ).
Supervised dimensionality reduction
Uncertainty Quantification
Data-driven polynomial chaos expansion for machine learning regression
We present a regression technique for data-driven problems based on polynomial chaos expansion (PCE).
Polynomial-Chaos-based Kriging
These two techniques have been developed more or less in parallel so far with little interaction between the researchers in the two fields.
