Articlegreen2017

Comparison of Surrogate-Based Uncertainty Quantification Methods for Computationally Expensive Simulators

Nathan Owen · Peter Challenor · Prathyush P. Menon · Samir Bennani

75 citations0/0 files storedSIAM/ASA Journal on Uncertainty Quantification

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Probabilistic and Robust Engineering Design Advanced Multi-Objective Optimization Algorithms Gaussian Processes and Bayesian Inference

Abstract

Polynomial chaos and Gaussian process emulation are methods for surrogate-based uncertainty quantification and have been developed independently in their respective communities over the last 25 years. Despite tackling similar problems in the field, to our knowledge there has yet to be a critical comparison of the two approaches in the literature. We begin by providing a detailed description of polynomial chaos and Gaussian process approaches for building a surrogate model of a black-box function. The accuracy of each surrogate method is then tested and compared for two simulators used in industry: a land-surface model (adJULES) and a launch vehicle controller (VEGACONTROL). We analyze surrogates built on experimental designs of various size and type to investigate their performance in a range of modeling scenarios. Specifically, polynomial chaos and Gaussian process surrogates are built on Sobol sequence and tensor grid designs. Their accuracy is measured by their ability to estimate the mean, standard deviation, exceedance probabilities, and probability density function of the simulator output, as well as a root mean square error metric, based on an independent validation design. We find that one method does not unanimously outperform the other, but advantages can be gained in some cases, such that the preferred method depends on the modeling goals of the practitioner. Our conclusions are likely to depend somewhat on the modeling choices for the surrogates as well as the design strategy. We hope that this work will spark future comparisons of the two methods in their more advanced formulations and for different sampling strategies.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 75 citations exist upstream.

References

1992STATISTICS FOR SPATIAL DATA 2000A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code 1989Design and Analysis of Computer Experiments 1991Stochastic Finite Elements: A Spectral Approach 2005High-Order Collocation Methods for Differential Equations with Random Inputs 2010Adaptive sparse polynomial chaos expansion based on least angle regression 2003Modeling uncertainty in flow simulations via generalized polynomial chaos 2004Probabilistic Sensitivity Analysis of Complex Models: A Bayesian Approach

Cited by

2019Uncertainty propagation of frequency response functions using a multi-output Gaussian Process model 2018Review and comparison of two meta-model-based uncertainty propagation analysis methods in groundwater applications: polynomial chaos expansion and Gaussian process emulation 2022An uncertainty-quantification framework for assessing accuracy, sensitivity, and robustness in computational fluid dynamics 2017Multi-Fidelity Uncertainty Analysis in CFD Using Hierarchical Kriging 2019

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.