Frequentist history matching with Interval Predictor Models

Sadeghi, Jonathan; Angelis, Marco de; Patelli, Edoardo

doi:10.1016/j.apm.2018.04.003

Cited by 12 publications

(3 citation statements)

References 12 publications

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“…Also radial basis functions [125], sparse grids [112], Artificial Neural Networks [124,65,160,38], Support Vector Machines [97,230] or Kriging interpolation schemes [120,84] have been applied in this context. Recently, also interval predictor models [21,31] have been introduced in this context and illustrated in range of applications [170,56]. Note that in fact, any surrogate model can be applied in this context.…”

Section: Global Optimization Approachmentioning

confidence: 99%

Recent Trends in the Modeling and Quantification of Non-probabilistic Uncertainty

Faes

Moens

2019

Arch Computat Methods Eng

129

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This paper gives an overview of recent advances in the field of nonprobabilistic uncertainty quantification. Both techniques for the forward propagation and inverse quantification of interval and fuzzy uncertainty are discussed. Also the modeling of spatial uncertainty in an interval and fuzzy context is discussed. An in depth discussion of a recently introduced method for the inverse quantification of spatial interval uncertainty is provided and its performance is illustrated using a case studies taken from literature. It is shown that the method enables an accurate quantification of spatial uncertainty under very low data availability and with a very limited amount of assumptions on the underlying uncertainty. Finally, also a conceptual comparison with the class of Bayesian methods for uncertainty quantification is provided.

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Section: Global Optimization Approachmentioning

confidence: 99%

Recent Trends in the Modeling and Quantification of Non-probabilistic Uncertainty

Faes

Moens

2019

Arch Computat Methods Eng

129

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“…In addition, Sadeghi et al [158] describe how to propagate mixed uncertainties in the form of p-boxes by IPMs. In [157] they combine IPMs with a Frequentist probabilistic framework.…”

Section: Interval Predictor Modelsmentioning

confidence: 99%

Unified Framework and Survey for Model Verification, Validation and Uncertainty Quantification

Riedmaier

Danquah

Schick

et al. 2020

Arch Computat Methods Eng

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Simulation is becoming increasingly important in the development, testing and approval process in many areas of engineering, ranging from finite element models to highly complex cyber-physical systems such as autonomous cars. Simulation must be accompanied by model verification, validation and uncertainty quantification (VV&UQ) activities to assess the inherent errors and uncertainties of each simulation model. However, the VV&UQ methods differ greatly between the application areas. In general, a major challenge is the aggregation of uncertainties from calibration and validation experiments to the actual model predictions under new, untested conditions. This is especially relevant due to high extrapolation uncertainties, if the experimental conditions differ strongly from the prediction conditions, or if the output quantities required for prediction cannot be measured during the experiments. In this paper, both the heterogeneous VV&UQ landscape and the challenge of aggregation will be addressed with a novel modular and unified framework to enable credible decision making based on simulation models. This paper contains a comprehensive survey of over 200 literature sources from many application areas and embeds them into the unified framework. In addition, this paper analyzes and compares the VV&UQ methods and the application areas in order to identify strengths and weaknesses and to derive further research directions. The framework thus combines a variety of VV&UQ methods, so that different engineering areas can benefit from new methods and combinations. Finally, this paper presents a procedure to select a suitable method from the framework for the desired application.

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“…In this paper, a novel technique is proposed which uses Interval Predictor Models -a recently developed type of metamodel which make very few assumptions [7,25] -to propagate distributional probability boxes through black box models. Interval Predictor Models intrinsically quantify uncertainty, and the reliability of this uncertainty quantification can be assessed using recent advancements in scenario optimisation [6] (i.e.…”

Section: Introductionmentioning

confidence: 99%

Robust propagation of probability boxes by interval predictor models

2020

Self Cite

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This paper proposes numerical strategies to robustly and efficiently propagate probability boxes through expensive black box models. An interval is obtained for the system failure probability, with a confidence level. The three proposed algorithms are sampling based, and so can be easily parallelised, and make no assumptions about the functional form of the model. In the first two algorithms, the performance function is modelled as a function with unknown noise structure in the aleatory space and supplemented by a modified performance function. In the third algorithm, an Interval Predictor Model is constructed and a re-weighting strategy used to find bounds on the probability of failure. Numerical examples are presented to show the applicability of the approach. The proposed method is flexible and can account for epistemic uncertainty contained inside the limit state function. This is a feature which, to the best of the authors' knowledge, no existing methods of this type can deal with.

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Frequentist history matching with Interval Predictor Models

Cited by 12 publications

References 12 publications

Recent Trends in the Modeling and Quantification of Non-probabilistic Uncertainty

Recent Trends in the Modeling and Quantification of Non-probabilistic Uncertainty

Unified Framework and Survey for Model Verification, Validation and Uncertainty Quantification

Robust propagation of probability boxes by interval predictor models

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