A Deep Surrogate Model for Robust and Generalizable Long-Term Blast Wave Prediction

• URL: http://arxiv.org/abs/2602.18168v1
• Date: Fri, 20 Feb 2026 12:14:28 GMT
• 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,
• Abstract summary: We propose RGD-Blast, a robust and generalizable deep surrogate model for high-fidelity, long-term blast wave forecasting.<n> RGD-Blast incorporates a multi-scale module to capture both global flow patterns and local boundary interactions, effectively mitigating error accumulation during autoregressive prediction.<n> Experiments demonstrate that RGD-Blast achieves a two-order-of-magnitude speedup while maintaining comparable accuracy.
• Score: 12.76666434787901
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

Related papers

• Scalable Spatio-Temporal SE(3) Diffusion for Long-Horizon Protein Dynamics [51.85385061275941]
  Molecular dynamics (MD) simulations remain the gold standard for studying protein dynamics.<n>Recent generative models have shown promise in accelerating simulations, yet they struggle with long-horizon generation.<n>We present STAR-MD, a scalable diffusion model that generates physically plausible protein trajectories over micro-scale timescales.
  arXiv  Detail & Related papers  (2026-02-02T14:13:28Z)
• SynCast: Synergizing Contradictions in Precipitation Nowcasting via Diffusion Sequential Preference Optimization [62.958457694151384]
  We introduce preference optimization into precipitation nowcasting for the first time, motivated by the success of reinforcement learning from human feedback in large language models.<n>In the first stage, the framework focuses on reducing FAR, training the model to effectively suppress false alarms.
  arXiv  Detail & Related papers  (2025-10-22T16:11:22Z)
• Differential-Integral Neural Operator for Long-Term Turbulence Forecasting [43.04613533979613]
  textbfunderlineDifferential-textbfunderlineIntegral textbfunderlineNeural textbfunderlineOperator (method)<n>method explicitly models the turbulent evolution through parallel branches that learn distinct physical operators.<n>It successfully suppresses error accumulation over hundreds of timesteps, maintains high fidelity in both the vorticity fields and energy spectra, and establishes a new benchmark for physically consistent, long-range turbulence forecast.
  arXiv  Detail & Related papers  (2025-09-25T14:08:26Z)
• Scale-Disentangled spatiotemporal Modeling for Long-term Traffic Emission Forecasting [10.643363557001955]
  We propose a Scale-Disd S-temporal Modeling (SDSTM) framework for long-term traffic emission forecasting.<n>The proposed model achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-08-16T06:06:47Z)
• Elucidated Rolling Diffusion Models for Probabilistic Weather Forecasting [52.6508222408558]
  We introduce Elucidated Rolling Diffusion Models (ERDM)<n>ERDM is the first framework to unify a rolling forecast structure with the principled, performant design of Elucidated Diffusion Models (EDM)<n>On 2D Navier-Stokes simulations and ERA5 global weather forecasting at 1.5circ resolution, ERDM consistently outperforms key diffusion-based baselines.
  arXiv  Detail & Related papers  (2025-06-24T21:44:31Z)
• Hierarchical Implicit Neural Emulators [20.09615751270837]
  We introduce a multiscale implicit neural emulator that enhances long-term prediction accuracy.<n>Our approach leverages predictions several steps ahead in time at increasing compression rates for next-timestep refinements.<n> Experiments on turbulent fluid dynamics show that our method achieves high short-term accuracy and produces long-term stable forecasts.
  arXiv  Detail & Related papers  (2025-06-05T00:28:26Z)
• Advanced long-term earth system forecasting by learning the small-scale nature [74.19833913539053]
  We present Triton, an AI framework designed to address this fundamental challenge.<n>Inspired by increasing grids to explicitly resolve small scales in numerical models, Triton employs a hierarchical architecture processing information across multiple resolutions to mitigate spectral bias.<n>We demonstrate Triton's superior performance on challenging forecast tasks, achieving stable year-long global temperature forecasts, skillful Kuroshio eddy predictions till 120 days, and high-fidelity turbulence simulations.
  arXiv  Detail & Related papers  (2025-05-26T02:49:00Z)
• TI-DeepONet: Learnable Time Integration for Stable Long-Term Extrapolation [0.0]
  TI-DeepONet is a framework that integrates neural operators with adaptive numerical time-stepping techniques.<n>This research establishes a physics-aware operator learning paradigm that bridges neural approximation with numerical analysis.
  arXiv  Detail & Related papers  (2025-05-22T23:24:31Z)
• Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
  Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models. Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML) We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
  arXiv  Detail & Related papers  (2024-07-18T15:44:23Z)
• Learning from Polar Representation: An Extreme-Adaptive Model for Long-Term Time Series Forecasting [10.892801642895904]
  We propose Distance-weighted Auto-regularized Neural network (DAN), a novel extreme-adaptive model for long-range forecasting of stremflow enhanced by polar representation learning. On four real-life hydrologic streamflow datasets, we demonstrate that DAN significantly outperforms both state-of-the-art hydrologic time series prediction methods and general methods designed for long-term time series prediction.
  arXiv  Detail & Related papers  (2023-12-14T09:16:01Z)
• Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems [65.44033635330604]
  We introduce a novel load forecasting method in which observed dynamics are modeled as a forced linear system. We show that its use of intrinsic linear dynamics offers a number of desirable properties in terms of interpretability and parsimony. Results are presented for a test case using load data from an electrical grid.
  arXiv  Detail & Related papers  (2020-10-08T20:25:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.