Abstract
High-fidelity finite-element simulations provide accurate crashworthiness predictions, but their cost limits iterative design exploration. Deep learning surrogates can reduce this cost, but many component-level models are developed under a single prescribed boundary condition, limiting generalisation to boundary variations. This work proposes a Boundary-Condition-Aware Transformer Neural Operator (BAT-NO) for autoregressive prediction of transient displacement fields and scalar crashworthiness responses under variations in geometry and boundary conditions. A B-pillar simulation framework evaluates generalisation across variations in geometry, impact position and velocity, and support stiffness. BAT-NO combines recurrent mesh processing with latent-grid Fourier operator processing. Boundary-condition information is transferred to the latent grid through a hybrid local--global mechanism. Slice-based attention models interactions among physically related regions, while direct boundary-to-grid projection preserves local spatial structure. Across the validation sets for the shape-only, shape-and-loading, and shape-loading-boundary cases, BAT-NO achieves the lowest mean final-step mean nodal Euclidean displacement error among the evaluated baselines. In the most challenging case, it reduces the mean error by 32.6% relative to the second-best model. Hyperparameter tuning reduces the validation error from 0.451 to 0.269 mm, with a comparable error of 0.267 mm on 300 unseen test simulations sampled within the investigated design space. An attention-based scalar decoder jointly predicts six response trajectories with a mean relative error of 2.46%. Most derived crashworthiness indicators have median errors below 3%. These results show that explicit local and global boundary-condition representations improve crashworthiness prediction over expanded component-level design spaces.
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Cite this article
APA 7
Li, H., Zhao, Y., Zhou, H., Ziane, M., Culiere, P., Pfaff, T., & Li, N. (2026). BAT-NO: A Boundary-Condition-Aware Transformer Neural Operator for Crashworthiness Prediction of Vehicle Components. https://omanscience.com/en/articles/bat-no-a-boundary-condition-aware-transformer-neural-operator-for-crashworthiness-prediction-of-vehicle-components
MLA 9
Li, Haoran, et al. "BAT-NO: A Boundary-Condition-Aware Transformer Neural Operator for Crashworthiness Prediction of Vehicle Components." https://omanscience.com/en/articles/bat-no-a-boundary-condition-aware-transformer-neural-operator-for-crashworthiness-prediction-of-vehicle-components.
Chicago (author–date)
Li, Haoran, Yingxue Zhao, Haosu Zhou, Mustapha Ziane, Pierre Culiere, Tobias Pfaff, and Nan Li. 2026. "BAT-NO: A Boundary-Condition-Aware Transformer Neural Operator for Crashworthiness Prediction of Vehicle Components." https://omanscience.com/en/articles/bat-no-a-boundary-condition-aware-transformer-neural-operator-for-crashworthiness-prediction-of-vehicle-components.
Harvard
Li, H., Zhao, Y., Zhou, H., Ziane, M., Culiere, P., Pfaff, T. and Li, N. (2026) 'BAT-NO: A Boundary-Condition-Aware Transformer Neural Operator for Crashworthiness Prediction of Vehicle Components', Available at: https://omanscience.com/en/articles/bat-no-a-boundary-condition-aware-transformer-neural-operator-for-crashworthiness-prediction-of-vehicle-components.
Vancouver
Li H, Zhao Y, Zhou H, Ziane M, Culiere P, Pfaff T, et al. BAT-NO: A Boundary-Condition-Aware Transformer Neural Operator for Crashworthiness Prediction of Vehicle Components. https://omanscience.com/en/articles/bat-no-a-boundary-condition-aware-transformer-neural-operator-for-crashworthiness-prediction-of-vehicle-components
IEEE
H. Li, Y. Zhao, H. Zhou, M. Ziane, P. Culiere, T. Pfaff, and N. Li, "BAT-NO: A Boundary-Condition-Aware Transformer Neural Operator for Crashworthiness Prediction of Vehicle Components," https://omanscience.com/en/articles/bat-no-a-boundary-condition-aware-transformer-neural-operator-for-crashworthiness-prediction-of-vehicle-components.