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Uncertainty quantification patterns for multiscale models.

D Ye1, L Veen2, A Nikishova1

  • 1Computational Science Lab, Informatics Institute, Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|March 29, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces uncertainty quantification patterns (UQPs) to analyze uncertainty in complex computational models. UQPs offer reusable building blocks for reliable, reproducible scientific simulations.

Keywords:
multiscale simulationsurrogate modellinguncertainty propagationuncertainty quantification

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Area of Science:

  • Computational Science
  • Scientific Computing
  • Modeling and Simulation

Background:

  • Uncertainty quantification (UQ) is crucial for computational models in decision-making.
  • Existing UQ methods can be complex for multi-scale and multi-domain applications.

Purpose of the Study:

  • To present reusable uncertainty quantification patterns (UQPs).
  • To support uncertainty analysis in coupled multi-scale and multi-domain applications.
  • To provide building blocks for tailored UQ in multiscale modeling.

Main Methods:

  • Implementation of UQPs as generic templates using the Multiscale Coupling Library and Environment 3.
  • Customization and aggregation of UQPs to create dedicated UQ procedures.
  • Demonstration with two proof-of-concept multiscale applications.

Main Results:

  • UQPs facilitate the creation of tailored UQ procedures for complex applications.
  • Potential for speed-up in UQ analysis using UQPs.
  • Successful application of UQPs in two multiscale scenarios.

Conclusions:

  • UQPs offer a structured approach to UQ for multiscale applications.
  • The presented framework enhances reliability and reproducibility in computational science.
  • UQPs are valuable for robust decision-making in the presence of model uncertainties.