Uncertainty Quantification

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5 domain-specific abstractions whose origin domain is Uncertainty Quantification.

• Gaussian process emulator — A probabilistic surrogate that uses a Gaussian process fitted to selected simulator runs to predict an expensive model's output and quantify interpolation uncertainty.
• Polynomial Chaos Expansion — Represent a finite-variance model response in polynomials orthogonal to the probability law of declared random inputs, enabling coefficient-based uncertainty propagation and moments.
• Probability Bounds Analysis — An imprecise-probability method that propagates lower and upper cumulative-distribution bounds through a model under declared dependence assumptions, enclosing all compatible output laws.
• Probability box — A pair of noncrossing lower and upper cumulative-distribution bounds representing a set of admissible probability distributions for an uncertain quantity.
• Variance-based sensitivity analysis — A global sensitivity method decomposing model-output variance into first-order and interaction contributions from uncertain inputs, commonly summarized by Sobol' indices.