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.
Tripnet: Learning large-scale high-fidelity 3d car aerodynamics with triplane networks
4 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Surrogate modeling has emerged as a powerful tool to accelerate Computational Fluid Dynamics (CFD) simulations. Existing 3D geometric learning models based on point clouds, voxels, meshes, or graphs depend on explicit geometric representations that are memory-intensive and resolution-limited. For large-scale simulations with millions of nodes and cells, existing models require aggressive downsampling due to their dependence on mesh resolution, resulting in degraded accuracy. We present TripNet, a triplane-based neural framework that implicitly encodes 3D geometry into a compact, continuous feature map with fixed dimension. Unlike mesh-dependent approaches, TripNet scales to high-resolution simulations without increasing memory cost, and enables CFD predictions at arbitrary spatial locations in a query-based fashion, independent of mesh connectivity or predefined nodes. TripNet achieves state-of-the-art performance on the DrivAerNet and DrivAerNet++ datasets, accurately predicting drag coefficients, surface pressure, and full 3D flow fields. With a unified triplane backbone supporting multiple simulation tasks, TripNet offers a scalable, accurate, and efficient alternative to traditional CFD solvers and existing surrogate models.
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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.
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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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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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.