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Generalized Multifidelity Active Learning for Gaussian-process-based Reliability Analysis

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Dynamic Data Driven Applications Systems (DDDAS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13984))

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Abstract

Efficient methods for achieving active learning in complex physical systems are essential for achieving the two-way interaction between data and models that underlies DDDAS. This work presents a two-stage multifidelity active learning method for Gaussian-process-based reliability analysis. In the first stage, the method allows for the flexibility of using any single-fidelity acquisition function for failure boundary identification when selecting the next sample location. We demonstrate the generalized multifidelity method using the existing acquisition functions of expected feasibility, U-learning, targeted integrated mean square error acquisition functions, or their a priori Monte Carlo sampled variants. The second stage uses a weighted information-gain-based criterion for the fidelity model selection. The multifidelity method leads to significant computational savings over the single-fidelity versions for real-time reliability analysis involving expensive physical system simulations.

This work has been supported in part by Department of Energy award number DE-SC0021239, ARPA-E Differentiate award number DE-AR0001208, and AFOSR DDIP award FA9550-22-1-0419.

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Authors and Affiliations

  1. Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, 78712, USA

    Anirban Chaudhuri & Karen Willcox

Authors
  1. Anirban Chaudhuri
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  2. Karen Willcox
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Correspondence to Anirban Chaudhuri .

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Editors and Affiliations

  1. MOVEJ Analytics, Fairborn, OH, USA

    Erik Blasch

  2. InfoSymbiotic Systems Society, Bethesda, MD, USA

    Frederica Darema

  3. Air Force Research Laboratories, Canastota, NY, USA

    Alex Aved

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Chaudhuri, A., Willcox, K. (2024). Generalized Multifidelity Active Learning for Gaussian-process-based Reliability Analysis. In: Blasch, E., Darema, F., Aved, A. (eds) Dynamic Data Driven Applications Systems. DDDAS 2022. Lecture Notes in Computer Science, vol 13984. Springer, Cham. https://doi.org/10.1007/978-3-031-52670-1_2

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  • DOI: https://doi.org/10.1007/978-3-031-52670-1_2

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  • Online ISBN: 978-3-031-52670-1

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