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Abstract Bayesian optimization (BO) is a sequential optimization strategy that is increasingly employed in a wide range of areas including materials design. In real world applications, acquiring high-fidelity (HF) data through physical experiments or HF simulations is the major cost component of BO. To alleviate this bottleneck, multi-fidelity (MF) methods are increasingly used to forgo the sole reliance on the expensive HF data and reduce the sampling costs by querying inexpensive low-fidelity (LF) sources whose data are correlated with HF samples. Existing multi-fidelity BO (MFBO) methods operate under the following two assumptions: (1) Leveraging global (rather than local) correlation between HF and LF sources, and (2) Associating all the data sources with the same noise process. These assumptions dramatically reduce the performance of MFBO when LF sources are only locally correlated with the HF source or when the noise variance varies across the data sources. To dispense with these incorrect assumptions, we propose an MF emulation method that (1) learns a noise model for each data source, and (2) enables BO to leverage highly biased LF sources which are only locally correlated with the HF source. We illustrate the performance of our method through analytical examples and engineering problems on materials design.
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Integrating Image Segmentation and Digital Human Modeling Through Multi-Fidelity Gaussian Process for Driver Visibility Analysis
Accurate assessment of driver visibility is crucial in automotive design and safety enhancement, particularly in situations where A-pillars obstruct the driver’s field of view. To address this challenge, this research develops a multi-fidelity Gaussian Process (MF-GP) modeling framework to enhance visibility prediction by integrating low-fidelity (LF) image segmentation data with high-fidelity digital human modeling (DHM) simulations. By leveraging a limited set of high-fidelity samples, the proposed MF-GP framework systematically calibrates low-fidelity data to improve predictive accuracy while reducing computational costs. Two A-pillar cutout designs (3.75 cm and 5 cm) were analyzed under varying HF sampling densities of 3%, 7%, and 10%. Results indicate that the 3.75 cm cutout is more sensitive to sparse HF sampling, requiring a denser HF dataset to achieve stable calibration. In contrast, the 5 cm cutout, benefiting from improved LF-HF alignment, achieves comparable accuracy with fewer HF samples. Model validation using root mean square error (RMSE) and coefficient of determination (R2) confirms that increasing HF sampling enhances surrogate model accuracy, with the effect being more pronounced in cases where model performance is susceptible to high-fidelity data. The proposed framework provides a computationally efficient methodology for driver visibility prediction and human-in-the-loop design applications. Future research could explore adaptive HF sampling strategies and ensemble surrogate modeling techniques to further enhance multi-fidelity learning efficiency.
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- Award ID(s):
- 2207408
- PAR ID:
- 10661923
- Publisher / Repository:
- American Society of Mechanical Engineers
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Conference Paper:
- https://doi.org/10.1115/DETC2025-169050
