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Life sciences · Preprint
arXiv · September 9, 2026
Early or partial results. Treat as a signal, not a conclusion.
A graph neural operator surrogate trained on synthetic LS-DYNA stress-tensor fields achieves cross-velocity field prediction with a single-step relative L2 error of 0.6977 and computational speedup of 3.6–4.3 thousand times over single-core LS-DYNA. The work is proof-of-concept within a narrow parameter space (single projectile and target geometry, 4 impact velocities) and does not validate the surrogate against independent experiments; it is positioned as a simulation-trained decision-support tool.
Computational surrogate model development with cross-velocity extrapolation validation. Virtual concrete target (diameter 500 mm, thickness 200 mm) impacted by projectile (diameter 45 mm, mass 2.13 kg) across four impact velocities (100, 135, 165, 200 m/s) and 100 aggregate microstructure configurations. Intervention: Graph neural operator surrogate model predicting time-varying six-component stress-tensor fields on the X-Z mid-plane. Compared with: Single-core LS-DYNA full-scale aggregate-resolved finite-element simulation. n = 400.
Single-step relative L2 error: 0.6977 for cross-velocity field prediction Autoregressive rollout from frame 11 to 39 completed in approximately 144 ms Computational speedup: 3.6×10³ to 4.3×10³ relative to single-core LS-DYNA
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
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This is a physics-informed machine learning surrogate model study using synthetic data from computational simulation, not experimental validation or clinical/translational endpoints; it demonstrates feasibility and speedup within a bounded parameter space but lacks independent experimental verification and generalizability outside the studied configurations.
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Although the impact resistance of concrete has been studied extensively, a framework linking mesoscale heterogeneity to full-field stress-tensor prediction has been lacking. Data were generated with a full-scale aggregate-resolved LS-DYNA model (projectile diameter 45 mm, mass 2.13 kg, target diameter 500 mm x thickness 200 mm, mesh 10 mm), verified against published penetration experiments (Frew 2006, Hanchak 1992, Forrestal 1996) by configuration similarity. The dataset contains six-component stress-tensor fields on the X-Z mid-plane for 400 cases (4 impact velocities x 100 aggregate seeds). Three contributions are reported. First, case-by-case verification of the terminal penetration state delimited the rest-state validity of penetration depth and anchored reliable observables to rigid-body motion and field-level stress evolution. Second, a field-level graph neural operator surrogate learned the time-varying stress-field evolution and evaluated cross-velocity leave-one-out extrapolation. Third, the full-scale, aggregate-resolved, cross-velocity, per-seed database was established as a reproducible resource. Cases at 100, 135 and 200 m/s still moved at window end (negative velocity, i.e. rebound), and only one 165 m/s case arrested. Penetration depth is therefore not reported as a rest-state scalar except for the single arrested case (69.33 mm); nose-node depth differences were confirmed as numerical artifacts of displacement integration after erosion. The single-step relative L2 error was 0.6977, reported honestly; autoregressive rollout from frame 11 to 39 took about 144 ms, a speedup of about 3.6x10^3 to 4.3x10^3 relative to single-core LS-DYNA, reported as application value. Validation is bounded by configuration similarity and field-level self-consistency; the framework is a simulation-trained decision-support method within the studied parameter space.
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