TY - GEN
T1 - Exploring multi-fidelity noisy data
T2 - 31st International Conference on Noise and Vibration Engineering, ISMA 2024 and 10th International Conference on Uncertainty in Structural Dynamics, USD 2024
AU - Giannoukou, K.
AU - Ascia, P.
AU - Marelli, S.
AU - Duddeck, F.
AU - Sudret, B.
N1 - Publisher Copyright:
© 2024 Proceedings of ISMA 2024 - International Conference on Noise and Vibration Engineering and USD 2024 - International Conference on Uncertainty in Structural Dynamics. All rights reserved.
PY - 2024
Y1 - 2024
N2 - Multi-fidelity surrogate models (MFSMs) are a well-established tool to combine information from sources with diverse computational fidelities into a single surrogate model. The sources of higher or lower fidelity can be, for example, computer simulations or physical experiments. MFSMs can exhibit enhanced predictive accuracy and reduced costs in emulating the response of complex systems, outperforming their single-fidelity surrogate model counterparts at comparable training costs. In real-world applications, uncertainty is present in the data, regardless of their fidelity. This uncertainty can be due to measurement noise, numerical noise, or unobserved/latent variables, and adds a layer of complexity by introducing non-deterministic behavior in the system response. In this work, we provide a framework to address the uncertainty in MFSM scenarios. The effectiveness of our approach is demonstrated through a transfer learning application in crashworthiness and a real-world wind turbine application, showcasing the applicability and versatility of our proposed methods.
AB - Multi-fidelity surrogate models (MFSMs) are a well-established tool to combine information from sources with diverse computational fidelities into a single surrogate model. The sources of higher or lower fidelity can be, for example, computer simulations or physical experiments. MFSMs can exhibit enhanced predictive accuracy and reduced costs in emulating the response of complex systems, outperforming their single-fidelity surrogate model counterparts at comparable training costs. In real-world applications, uncertainty is present in the data, regardless of their fidelity. This uncertainty can be due to measurement noise, numerical noise, or unobserved/latent variables, and adds a layer of complexity by introducing non-deterministic behavior in the system response. In this work, we provide a framework to address the uncertainty in MFSM scenarios. The effectiveness of our approach is demonstrated through a transfer learning application in crashworthiness and a real-world wind turbine application, showcasing the applicability and versatility of our proposed methods.
UR - http://www.scopus.com/inward/record.url?scp=85212219432&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85212219432
T3 - Proceedings of ISMA 2024 - International Conference on Noise and Vibration Engineering and USD 2024 - International Conference on Uncertainty in Structural Dynamics
SP - 4161
EP - 4171
BT - Proceedings of ISMA 2024 - International Conference on Noise and Vibration Engineering and USD 2024 - International Conference on Uncertainty in Structural Dynamics
A2 - Desmet, W.
A2 - Pluymers, B.
A2 - Moens, D.
A2 - del Fresno Zarza, J.
PB - KU Leuven, Departement Werktuigkunde
Y2 - 9 September 2024 through 11 September 2024
ER -