Accurately modeling the spatio-temporal dynamics of blast wave propagation remains a longstanding challenge due to its highly nonlinear behavior, sharp gradients, and burdensome computational cost. While machine learning-based surrogate models offer fast inference as a promising alternative, they suffer from degraded accuracy, particularly evaluated on complex urban layouts or out-of-distribution scenarios. Moreover, autoregressive prediction strategies in such models are prone to error accumulation over long forecasting horizons, limiting their robustness for extended-time simulations. To address these limitations, we propose RGD-Blast, a robust and generalizable deep surrogate model for high-fidelity, long-term blast wave forecasting. RGD-Blast incorporates a multi-scale module to capture both global flow patterns and local boundary interactions, effectively mitigating error accumulation during autoregressive prediction. We introduce a dynamic-static feature coupling mechanism that fuses time-varying pressure fields with static source and layout features, thereby enhancing out-of-distribution generalization. Experiments demonstrate that RGD-Blast achieves a two-order-of-magnitude speedup over traditional numerical methods while maintaining comparable accuracy. In generalization tests on unseen building layouts, the model achieves an average RMSE below 0.01 and an R2 exceeding 0.89 over 280 consecutive time steps. Additional evaluations under varying blast source locations and explosive charge weights further validate its generalization, substantially advancing the state of the art in long-term blast wave modeling.

翻译：准确建模爆炸波传播的时空动力学仍然是一个长期存在的挑战，这归因于其高度非线性行为、陡峭梯度以及高昂的计算成本。尽管基于机器学习的代理模型提供了快速推理作为有前景的替代方案，但其准确性存在下降，尤其是在复杂城市布局或分布外场景下进行评估时。此外，此类模型中的自回归预测策略容易在长期预测范围内产生误差累积，限制了其在扩展时间模拟中的鲁棒性。为了解决这些局限性，我们提出了RGD-Blast，一种用于高保真、长期爆炸波预测的鲁棒且可泛化的深度代理模型。RGD-Blast包含一个多尺度模块，用于捕获全局流动模式和局部边界相互作用，从而有效缓解自回归预测过程中的误差累积。我们引入了一种动态-静态特征耦合机制，将时变压力场与静态源及布局特征相融合，从而增强了分布外泛化能力。实验表明，RGD-Blast在保持相当准确性的同时，相比传统数值方法实现了两个数量级的加速。在对未见过的建筑布局进行的泛化测试中，该模型在连续280个时间步长上实现了平均RMSE低于0.01且R2超过0.89的性能。在不同爆炸源位置和炸药当量下的进一步评估进一步验证了其泛化能力，显著推进了长期爆炸波建模的技术水平。