Neural statistical functions use prefix statistics to unify and directly predict statistical quantities over continuous ranges from pre-trained single-sample models without repeated sampling.
Automotive crash dynamics modeling accelerated with machine learning
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
CarCrashNet releases a large-scale open benchmark dataset of structural crash simulations and a hierarchical neural solver for data-driven full-vehicle crash prediction.
Hybrid mesh GNNs with geometry-aware attention achieve 3.20 mm temporal RMSE on a 25-sample full-vehicle lateral pole-impact test set while preserving interpretable displacement fields.
citing papers explorer
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Neural Statistical Functions
Neural statistical functions use prefix statistics to unify and directly predict statistical quantities over continuous ranges from pre-trained single-sample models without repeated sampling.
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CarCrashNet: A Large-Scale Dataset and Hierarchical Neural Solver for Data-Driven Structural Crash Simulation
CarCrashNet releases a large-scale open benchmark dataset of structural crash simulations and a hierarchical neural solver for data-driven full-vehicle crash prediction.
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Crash Assessment via Mesh-Based Graph Neural Networks and Physics-Aware Attention
Hybrid mesh GNNs with geometry-aware attention achieve 3.20 mm temporal RMSE on a 25-sample full-vehicle lateral pole-impact test set while preserving interpretable displacement fields.