Aero‐structural shape design and optimization of bridge decks rely on accurately estimating their self‐excited aeroelastic forces within the design domain. The inherent nonlinear features of bluff body aerodynamics and the high cost of wind tunnel tests and computational fluid dynamics (CFD) simulations make their emulation as a function of deck shape and reduced velocity challenging. State‐of‐the‐art methods address deck shape tailoring by interpolating discrete values of integrated flutter derivatives (FDs) in the frequency domain. Nevertheless, more sophisticated strategies can improve surrogate accuracy and potentially reduce the required number of samples. We propose a time domain emulation strategy harnessing temporal fusion transformers (TFTs) to predict the self‐excited forces time series before their integration into FDs. Emulating aeroelastic forces in the time domain permits the inclusion of time‐series amplitudes, frequencies, phases, and other properties in the training process, enabling a more solid learning strategy that is independent of the self‐excited forces modeling order and the inherent loss of information during the identification of FDs. TFTs' long‐ and short‐term context awareness, combined with their interpretability and enhanced ability to deal with static and time‐dependent covariates, make them an ideal choice for predicting unseen aeroelastic forces time series. The proposed TFT‐based metamodel offers a powerful technique for drastically improving the accuracy and versatility of wind‐resistant design optimization frameworks.

中文翻译：

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通过时间融合变压器进行气动弹性力预测


桥面的航空结构形状设计和优化依赖于在设计域内准确估计其自激气动弹性力。钝体空气动力学固有的非线性特性以及风洞测试和计算流体动力学 （CFD） 模拟的高成本使其作为甲板形状和降低速度的函数进行仿真具有挑战性。最先进的方法通过在频域中插入集成颤振导数 （FD） 的离散值来解决甲板形状定制问题。然而，更复杂的策略可以提高代理项的准确性，并可能减少所需的样本数量。我们提出了一种时域仿真策略，利用时间融合变压器 （TFT） 来预测自激力的时间序列，然后再将其集成到 FD 中。在时域中模拟气动弹性力允许在训练过程中包含时间序列振幅、频率、相位和其他属性，从而实现更可靠的学习策略，该策略独立于自激力建模顺序和过程中固有的信息损失TFT 的长期和短期上下文感知，结合它们的可解释性和处理静态和时间依赖性协变量的增强能力，使其成为预测看不见的气动弹性力时间序列的理想选择。所提出的基于 TFT 的元模型提供了一种强大的技术，可以大幅提高抗风设计优化框架的准确性和多功能性。