Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 71-86 |
| Seitenumfang | 16 |
| Fachzeitschrift | Chinese journal of aeronautics |
| Jahrgang | 37 |
| Ausgabenummer | 12 |
| Frühes Online-Datum | 16 Mai 2024 |
| Publikationsstatus | Veröffentlicht - Dez. 2024 |
Abstract
Interval Uncertainty Propagation (IUP) holds significant importance in quantifying uncertainties in structural outputs when confronted with interval input parameters. In the aviation field, the precise determination of probability models for input parameters of aeronautical structures entails substantial costs in both time and finances. As an alternative, the use of interval variables to describe input parameter uncertainty becomes a pragmatic approach. The complex task of solving the IUP for aeronautical structures, particularly in scenarios marked by pronounced nonlinearity and multiple outputs, necessitates innovative methodologies. This study introduces an efficient deep learning-driven approach to address the challenges associated with IUP. The proposed approach combines the Deep Neural Network (DNN) with intelligent optimization algorithms for dealing with the IUP in aeronautical structures. An inventive extremal value-oriented weighting technique is presented, assigning varying weights to different training samples within the loss function, thereby enhancing the computational accuracy of the DNN in predicting extremal values of structural outputs. Moreover, an adaptive framework is established to strategically balance the global exploration and local exploitation capabilities of the DNN, resulting in a predictive model that is both robust and accurate. To illustrate the effectiveness of the developed approach, various applications are explored, including a high-dimensional numerical example and two aeronautical structures. The obtained results highlight the high computational accuracy and efficiency achieved by the proposed approach, showcasing its potential for addressing complex IUP challenges in aeronautical engineering.
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- Luft- und Raumfahrttechnik
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in: Chinese journal of aeronautics, Jahrgang 37, Nr. 12, 12.2024, S. 71-86.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Deep learning-driven interval uncertainty propagation for aeronautical structures
T2 - Adaptive combination of line sampling for imprecise time-variant reliability analysis
AU - Shi, Yan
AU - BEER, Michael
N1 - Publisher Copyright: © 2024
PY - 2024/12
Y1 - 2024/12
N2 - Interval Uncertainty Propagation (IUP) holds significant importance in quantifying uncertainties in structural outputs when confronted with interval input parameters. In the aviation field, the precise determination of probability models for input parameters of aeronautical structures entails substantial costs in both time and finances. As an alternative, the use of interval variables to describe input parameter uncertainty becomes a pragmatic approach. The complex task of solving the IUP for aeronautical structures, particularly in scenarios marked by pronounced nonlinearity and multiple outputs, necessitates innovative methodologies. This study introduces an efficient deep learning-driven approach to address the challenges associated with IUP. The proposed approach combines the Deep Neural Network (DNN) with intelligent optimization algorithms for dealing with the IUP in aeronautical structures. An inventive extremal value-oriented weighting technique is presented, assigning varying weights to different training samples within the loss function, thereby enhancing the computational accuracy of the DNN in predicting extremal values of structural outputs. Moreover, an adaptive framework is established to strategically balance the global exploration and local exploitation capabilities of the DNN, resulting in a predictive model that is both robust and accurate. To illustrate the effectiveness of the developed approach, various applications are explored, including a high-dimensional numerical example and two aeronautical structures. The obtained results highlight the high computational accuracy and efficiency achieved by the proposed approach, showcasing its potential for addressing complex IUP challenges in aeronautical engineering.
AB - Interval Uncertainty Propagation (IUP) holds significant importance in quantifying uncertainties in structural outputs when confronted with interval input parameters. In the aviation field, the precise determination of probability models for input parameters of aeronautical structures entails substantial costs in both time and finances. As an alternative, the use of interval variables to describe input parameter uncertainty becomes a pragmatic approach. The complex task of solving the IUP for aeronautical structures, particularly in scenarios marked by pronounced nonlinearity and multiple outputs, necessitates innovative methodologies. This study introduces an efficient deep learning-driven approach to address the challenges associated with IUP. The proposed approach combines the Deep Neural Network (DNN) with intelligent optimization algorithms for dealing with the IUP in aeronautical structures. An inventive extremal value-oriented weighting technique is presented, assigning varying weights to different training samples within the loss function, thereby enhancing the computational accuracy of the DNN in predicting extremal values of structural outputs. Moreover, an adaptive framework is established to strategically balance the global exploration and local exploitation capabilities of the DNN, resulting in a predictive model that is both robust and accurate. To illustrate the effectiveness of the developed approach, various applications are explored, including a high-dimensional numerical example and two aeronautical structures. The obtained results highlight the high computational accuracy and efficiency achieved by the proposed approach, showcasing its potential for addressing complex IUP challenges in aeronautical engineering.
KW - Aeronautical structure
KW - Deep learning
KW - Interval variable
KW - Optimization algorithm
KW - Uncertainty propagation
UR - http://www.scopus.com/inward/record.url?scp=85207275535&partnerID=8YFLogxK
U2 - 10.1016/j.cja.2024.05.009
DO - 10.1016/j.cja.2024.05.009
M3 - Article
AN - SCOPUS:85207275535
VL - 37
SP - 71
EP - 86
JO - Chinese journal of aeronautics
JF - Chinese journal of aeronautics
SN - 1000-9361
IS - 12
ER -