BlastOFormer: Attention and Neural Operator Deep Learning Methods for Explosive Blast Prediction

• URL: http://arxiv.org/abs/2505.20454v1
• Date: Mon, 26 May 2025 18:47:50 GMT
• Title: BlastOFormer: Attention and Neural Operator Deep Learning Methods for Explosive Blast Prediction
• Abstract summary: BlastOFormer is a Transformer based surrogate model for full field maximum pressure prediction.<n>It is trained on a dataset generated using the open source blastFoam CFD solver.<n>It requires only 6.4 milliseconds for inference, more than 600,000 times faster than CFD simulations.
• Score: 6.349503549199403
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Accurate prediction of blast pressure fields is essential for applications in structural safety, defense planning, and hazard mitigation. Traditional methods such as empirical models and computational fluid dynamics (CFD) simulations offer limited trade offs between speed and accuracy; empirical models fail to capture complex interactions in cluttered environments, while CFD simulations are computationally expensive and time consuming. In this work, we introduce BlastOFormer, a novel Transformer based surrogate model for full field maximum pressure prediction from arbitrary obstacle and charge configurations. BlastOFormer leverages a signed distance function (SDF) encoding and a grid to grid attention based architecture inspired by OFormer and Vision Transformer (ViT) frameworks. Trained on a dataset generated using the open source blastFoam CFD solver, our model outperforms convolutional neural networks (CNNs) and Fourier Neural Operators (FNOs) across both log transformed and unscaled domains. Quantitatively, BlastOFormer achieves the highest R2 score (0.9516) and lowest error metrics, while requiring only 6.4 milliseconds for inference, more than 600,000 times faster than CFD simulations. Qualitative visualizations and error analyses further confirm BlastOFormer's superior spatial coherence and generalization capabilities. These results highlight its potential as a real time alternative to conventional CFD approaches for blast pressure estimation in complex environments.

Related papers

• PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting [62.22445401483844]
  Existing methods are severely constrained by the bottleneck of compounding errors.<n>We propose a universal framework called -Cast-Corrector (Plug-and-Play Corrector)<n>Our method significantly enhances the long-term stability of coupled forecasting systems.
  arXiv  Detail & Related papers  (2026-05-09T13:12:33Z)
• Physics-Informed Reinforcement Learning of Spatial Density Velocity Potentials for Map-Free Racing [73.88859384645264]
  Out-Of-Distribution (OOD) generalization to various racetracks utilizes Machine Learning (ML) to encode the mathematical relation between sensor data and vehicle actuation for end-to-end control.<n>This paper presents a DRL method that parameterizes nonlinear vehicle dynamics from the spectral distribution of depth measurements with a non-geometric, physics-informed reward, to infer vehicle time-optimal and overtaking racing controls with an Artificial Neural Network (ANN)<n>The policy outperforms human demonstrations by 12% in OOD tracks on proportionally scaled hardware, by maximizing the friction circle with tire dynamics that resemble an empirical Pacejka tire model.
  arXiv  Detail & Related papers  (2026-04-10T17:12:07Z)
• Advancing accelerator virtual beam diagnostics through latent evolution modeling: an integrated solution to forward, inverse, tuning, and UQ problems [46.568487965999225]
  We propose Latent Evolution Model (LEM), a hybrid machine learning framework with an autoencoder that projects high-dimensional phase spaces into lower-dimensional representations, and transformers to learn temporal dynamics in the latent space.<n>For textitforward modeling, a CVAE encodes 15 unique projections of the 6D phase space into a latent representation, while a transformer predicts downstream latent states from upstream inputs.<n>For textitinverse problems, we address two distinct challenges: (a) predicting upstream phase spaces from downstream observations, and (b) estimating RF settings from the latent space of
  arXiv  Detail & Related papers  (2026-02-26T04:46:26Z)
• SA-EMO: Structure-Aligned Encoder Mixture of Operators for Generalizable Full-waveform Inversion [0.0]
  Full-waveform inversion can produce high-resolution models, but it remains inherently ill-posed, highly nonlinear, and computationally intensive.<n>We propose a Structure-Aligned-Mixture-of-Operators (SA-EMO) architecture for velocity-field inversion under unknown subsurface structures.<n>SA-EMO significantly outperforms traditional CNN or single-operator methods, achieving an average MAE reduction of approximately 58.443% and an improvement in boundary resolution of about 10.308%.
  arXiv  Detail & Related papers  (2025-11-07T14:03:43Z)
• Unveiling the Spatial-temporal Effective Receptive Fields of Spiking Neural Networks [47.413471945080566]
  Spiking Neural Networks (SNNs) demonstrate significant potential for energy-efficient neuromorphic computing.<n>In artificial neural networks, the effective receptive field (ERF) serves as a valuable tool for analyzing feature extraction capabilities.<n>We introduce the Spatio-Temporal Effective Receptive Field (ST-ERF) to analyze the ERF distributions across various Transformer-based SNNs.
  arXiv  Detail & Related papers  (2025-10-24T12:46:58Z)
• Component-Based Machine Learning for Indoor Flow and Temperature Fields Prediction Latent Feature Aggregation and Flow Interaction [22.85411632321514]
  This study proposes a component-based machine learning (CBML) surrogate modeling approach to replace conventional CFD simulation.<n>CBML consists of three neural networks: a convolutional autoencoder with residual connections (CAER), a multilayer perceptron (MLP) and a convolutional neural network (CNN)<n>Results show that the CBML model accurately and fast predicts two-dimensional aggregated velocity fields across both training and testing datasets.
  arXiv  Detail & Related papers  (2025-07-25T12:57:30Z)
• Towards real-time assessment of infrasound event detection capability using deep learning-based transmission loss estimation [5.601176010173589]
  Accurate modeling of infrasound transmission loss is essential for evaluating the performance of the International Monitoring System.<n>State-of-the-art propagation modeling tools enable transmission loss to be finely simulated using atmospheric models.<n>Recent studies made use of a deep learning algorithm capable of making transmission loss predictions almost instantaneously.<n>In this study, we address these limitations by using both wind and temperature fields as inputs to a neural network, simulated up to 130 km altitude and 4,000 km distance.
  arXiv  Detail & Related papers  (2025-06-03T09:49:12Z)
• Deep learning methods for modeling infrasound transmission loss in the middle atmosphere [5.842419815638353]
  We develop an optimized convolutional network designed to minimize prediction errors while predicting TLs from globally simulated combined temperature and wind fields.<n>The implemented model predicts TLs with an average error of 8.6 dB in the whole frequency band (0.1-3.2 Hz) and explored realistic atmospheric scenarios.
  arXiv  Detail & Related papers  (2025-06-02T13:10:29Z)
• FLEX: A Backbone for Diffusion-Based Modeling of Spatio-temporal Physical Systems [51.15230303652732]
  FLEX (F Low EXpert) is a backbone architecture for generative modeling of-temporal physical systems.<n>It reduces the variance of the velocity field in the diffusion model, which helps stabilize training.<n>It achieves accurate predictions for super-resolution and forecasting tasks using as few features as two reverse diffusion steps.
  arXiv  Detail & Related papers  (2025-05-23T00:07:59Z)
• LaPON: A Lagrange's-mean-value-theorem-inspired operator network for solving PDEs and its application on NSE [8.014720523981385]
  We propose LaPON, an operator network inspired by the Lagrange's mean value theorem.<n>It embeds prior knowledge directly into the neural network architecture instead of the loss function.<n>LaPON provides a scalable and reliable solution for high-fidelity fluid dynamics simulation.
  arXiv  Detail & Related papers  (2025-05-18T10:45:17Z)
• Comparison of CNN-based deep learning architectures for unsteady CFD acceleration on small datasets [0.0]
  This study compares advanced convolutional neural network (CNN) architectures for accelerating unsteady computational fluid dynamics (CFD) simulations.<n>CNNs were evaluated under identical conditions to determine their predictive accuracy and robustness in autoregressive time-series predictions.<n> ConvLSTM-UNet consistently outperformed other models, particularly in difference value calculation, achieving lower maximum errors and stable residuals.
  arXiv  Detail & Related papers  (2025-02-06T03:30:49Z)
• STONet: A neural operator for modeling solute transport in micro-cracked reservoirs [0.49998148477760973]
  We introduce a novel neural operator, the Solute Transport Operator Network (STONet), to efficiently model contaminant transport in micro-cracked porous media.<n>Our numerical experiments demonstrate that, once trained, STONet achieves accurate predictions, with relative errors typically below 1% compared with FEM simulations.
  arXiv  Detail & Related papers  (2024-12-07T07:53:47Z)
• Reducing Spatial Discretization Error on Coarse CFD Simulations Using an OpenFOAM-Embedded Deep Learning Framework [0.7223509567556214]
  We propose a method for reducing the spatial discretization error of fluid dynamics problems by enhancing the quality of simulations using deep learning. We feed the model with fine-grid data after projecting it to the coarse-grid discretization. We substitute the default differencing scheme for the convection term by a feed-forward neural network that interpolates velocities from cell centers to face values to produce velocities that approximate the down-sampled fine-grid data well.
  arXiv  Detail & Related papers  (2024-05-13T02:59:50Z)
• Machine Learning model for gas-liquid interface reconstruction in CFD numerical simulations [59.84561168501493]
  The volume of fluid (VoF) method is widely used in multi-phase flow simulations to track and locate the interface between two immiscible fluids. A major bottleneck of the VoF method is the interface reconstruction step due to its high computational cost and low accuracy on unstructured grids. We propose a machine learning enhanced VoF method based on Graph Neural Networks (GNN) to accelerate the interface reconstruction on general unstructured meshes.
  arXiv  Detail & Related papers  (2022-07-12T17:07:46Z)
• Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management [64.17887333976593]
  Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO2 sequestration and wastewater injection. Managing the pressures by controlling injection/extraction are challenging because of complex heterogeneity in the subsurface. We use differentiable programming with a full-physics model and machine learning to determine the fluid extraction rates that prevent over-pressurization.
  arXiv  Detail & Related papers  (2022-06-21T20:38:13Z)
• Multi-fidelity Hierarchical Neural Processes [79.0284780825048]
  Multi-fidelity surrogate modeling reduces the computational cost by fusing different simulation outputs. We propose Multi-fidelity Hierarchical Neural Processes (MF-HNP), a unified neural latent variable model for multi-fidelity surrogate modeling. We evaluate MF-HNP on epidemiology and climate modeling tasks, achieving competitive performance in terms of accuracy and uncertainty estimation.
  arXiv  Detail & Related papers  (2022-06-10T04:54:13Z)
• Fast and Accurate Error Simulation for CNNs against Soft Errors [64.54260986994163]
  We present a framework for the reliability analysis of Conal Neural Networks (CNNs) via an error simulation engine. These error models are defined based on the corruption patterns of the output of the CNN operators induced by faults. We show that our methodology achieves about 99% accuracy of the fault effects w.r.t. SASSIFI, and a speedup ranging from 44x up to 63x w.r.t.FI, that only implements a limited set of error models.
  arXiv  Detail & Related papers  (2022-06-04T19:45:02Z)
• An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions [73.4962254843935]
  We study the capability of artificial neural network models to emulate storm surge based on the storm track/size/intensity history. This study presents a neural network model that can predict storm surge, informed by a database of synthetic storm simulations.
  arXiv  Detail & Related papers  (2022-04-18T23:42:18Z)
• Physics-aware deep neural networks for surrogate modeling of turbulent natural convection [0.0]
  We investigate the use of PINNs surrogate modeling for turbulent Rayleigh-B'enard convection flows. We show how it comes to play as a regularization close to the training boundaries which are zones of poor accuracy for standard PINNs. The predictive accuracy of the surrogate over the entire half a billion DNS coordinates yields errors for all flow variables ranging between [0.3% -- 4%] in the relative L 2 norm.
  arXiv  Detail & Related papers  (2021-03-05T09:48:57Z)

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.