GANO is an end-to-end differentiable latent-space optimizer that unifies shape encoding, surrogate prediction, and controllable geometry updates for PDE-governed shape optimization and inversion.
arXiv preprint arXiv:2503.17400 , year=
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BlendedNet++ provides a new dataset of 12,492 BWB geometries with RANS-derived Cp and Cf fields and benchmarks geometric deep learning for field prediction plus conditional diffusion models for inverse design achieving R^2 > 0.99 on lift-to-drag targets verified by CFD.
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.
citing papers explorer
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Geometry-Aware Neural Optimizer for Shape Optimization and Inversion
GANO is an end-to-end differentiable latent-space optimizer that unifies shape encoding, surrogate prediction, and controllable geometry updates for PDE-governed shape optimization and inversion.
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BlendedNet++: A dataset and benchmark for field-resolved aerodynamics and inverse design of blended wing body aircraft
BlendedNet++ provides a new dataset of 12,492 BWB geometries with RANS-derived Cp and Cf fields and benchmarks geometric deep learning for field prediction plus conditional diffusion models for inverse design achieving R^2 > 0.99 on lift-to-drag targets verified by CFD.
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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.