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[2602.18168] A Deep Surrogate Model for Robust and Generalizable Long-Term Blast Wave Prediction
Summary
The paper presents RGD-Blast, a deep surrogate model designed for accurate long-term blast wave prediction, addressing challenges in computational efficiency and generalization across diverse scenarios.
Why It Matters
This research is significant as it enhances the predictive capabilities of blast wave modeling, which is crucial for safety in urban environments and military applications. By improving speed and accuracy, the model can facilitate better decision-making in emergency response and urban planning.
Key Takeaways
- RGD-Blast significantly reduces computational time while maintaining accuracy in blast wave predictions.
- The model incorporates a multi-scale module to improve robustness against error accumulation.
- Dynamic-static feature coupling enhances the model's ability to generalize across different scenarios.
- Achieves an RMSE below 0.01 and an R2 exceeding 0.89 in unseen building layouts.
- Demonstrates advancements in the state of the art for long-term blast wave modeling.
Computer Science > Machine Learning arXiv:2602.18168 (cs) [Submitted on 20 Feb 2026] Title:A Deep Surrogate Model for Robust and Generalizable Long-Term Blast Wave Prediction Authors:Danning Jing, Xinhai Chen, Xifeng Pu, Jie Hu, Chao Huang, Xuguang Chen, Qinglin Wang, Jie Liu View a PDF of the paper titled A Deep Surrogate Model for Robust and Generalizable Long-Term Blast Wave Prediction, by Danning Jing and 6 other authors View PDF HTML (experimental) Abstract: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 fie...
