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February 11, 2026Structural and Multidisciplinary Optimization

An augmented autoregressive nonlinear mapping multi-fidelity surrogate model construction method

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Authors

ZTZongrui TianDalian University of TechnologyWHWanxin HeNingbo UniversityCWChen WangDalian University of Technology

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Implication

The proposed surrogate model construction improves predictions in engineering design, indicating greater data fusion efficiency.

Key Points

  • To introduce a novel surrogate model that enhances predictions and refines data fusion in engineering applications.
  • Constructed low-fidelity surrogate models from available datasets.
  • Integrated predicted low-fidelity and high-fidelity data into a trend function for regression.
  • Refined the multi-fidelity model by tuning hyperparameters.
  • AANMMF achieved accurate predictions with minimal computational cost.
  • Demonstrated performance improvement over a bi-fidelity surrogate model and a standard multi-fidelity model.
  • Tested on four numerical functions and one engineering case study, showing robustness.

Cite This Study

Tian et al. (2026) studied this question.

synapsesocial.com/papers/698be001058ab1890a13bb8chttps://doi.org/10.1007/s00158-025-04207-4
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