FLOATBench is a tabular benchmark dataset with 582,120 fatigue labels from 19,404 OpenFAST simulations of three 22 MW FOWT towers, featuring alpha-shape regime partitioning and three evaluation protocols for surrogate models.
arXiv preprint arXiv:2512.07847 , year =
6 Pith papers cite this work. Polarity classification is still indexing.
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A canonical engineering graph representation combined with region-aware graph attention learning enables robust and transferable 3D mode shape classification across heterogeneous vehicle models and sensor layouts.
CarCrashNet supplies a large multi-modal crash simulation benchmark and CrashSolver neural model for data-driven full-vehicle crash prediction, validated against experiments and commercial solvers.
LoRA adapters enable a 61.47M-parameter aerodynamics Transformer pretrained on four vehicle families to adapt to a held-out fifth family with 20 samples, reaching R²=0.85 and outperforming full fine-tuning and from-scratch training with 3x more data.
GTF-Net combines triplane features, AFNO spectral mixing, CNN refinement, and explicit geometric encodings to reduce relative L2 error on vehicle pressure and shear stress prediction versus prior baselines.
A graph learning framework turns heterogeneous 3D engineering data into physics-aware graphs processed by GNNs for CAE mode classification and CFD field prediction in automotive applications.
citing papers explorer
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FLOATBench: A Dataset and Benchmark for Floating Offshore Wind Turbine Tower Fatigue
FLOATBench is a tabular benchmark dataset with 582,120 fatigue labels from 19,404 OpenFAST simulations of three 22 MW FOWT towers, featuring alpha-shape regime partitioning and three evaluation protocols for surrogate models.
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Robust and Explainable 3D Mode Shape Recognition Using Region-Aware Graph Neural Networks
A canonical engineering graph representation combined with region-aware graph attention learning enables robust and transferable 3D mode shape classification across heterogeneous vehicle models and sensor layouts.
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CarCrashNet: A Large-Scale Dataset and Hierarchical Neural Solver for Data-Driven Structural Crash Simulation
CarCrashNet supplies a large multi-modal crash simulation benchmark and CrashSolver neural model for data-driven full-vehicle crash prediction, validated against experiments and commercial solvers.
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Adapting Automotive Aerodynamics Surrogates to New Vehicle Families via Transfer Learning
LoRA adapters enable a 61.47M-parameter aerodynamics Transformer pretrained on four vehicle families to adapt to a held-out fifth family with 20 samples, reaching R²=0.85 and outperforming full fine-tuning and from-scratch training with 3x more data.
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A Geometry-Aware Triplane Field Network for Vehicle Aerodynamic Prediction
GTF-Net combines triplane features, AFNO spectral mixing, CNN refinement, and explicit geometric encodings to reduce relative L2 error on vehicle pressure and shear stress prediction versus prior baselines.
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Toward Generalizable Graph Learning for 3D Engineering AI: Explainable Workflows for CAE Mode Shape Classification and CFD Field Prediction
A graph learning framework turns heterogeneous 3D engineering data into physics-aware graphs processed by GNNs for CAE mode classification and CFD field prediction in automotive applications.