Adaptive Multi-Fidelity Framework for Defect and Damage Tolerant Composite Structures
Dario Zamani, Chiara Franceschini, Marco Petrolo, Alfonso Pagani, Erasmo Carrera
Abstract. This work presents an adaptive multi-fidelity framework for uncertainty quantification in defect-tolerant composite structures. The approach couples the Carrera Unified Formulation (CUF) for generating consistent low- and high-fidelity structural theories with an adaptive Gaussian Process Regression (GPR) surrogate that learns their correlation. High-fidelity simulations are selectively activated in regions of high uncertainty, minimizing computational cost while preserving accuracy. The framework enables probabilistic estimation of stresses and failure indices with confidence intervals and is validated against 3D Abaqus benchmarks.
Keywords
Multi-Fidelity, Gaussian Process Regression, Carrera Unified Formulation, Uncertainty Quantification, Damage Tolerance
Published online 7/20/2026, 6 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Dario Zamani, Chiara Franceschini, Marco Petrolo, Alfonso Pagani, Erasmo Carrera, Adaptive Multi-Fidelity Framework for Defect and Damage Tolerant Composite Structures, Materials Research Proceedings, Vol. 69, pp 1181-1186, 2026
DOI: https://doi.org/10.21741/9781644904251-203
The article was published as article 203 of the book CEAS – AIDAA Conference 2025
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
References
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