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:2510.22491 , year=
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GeoFunFlow-3D reduces pressure-field RRMSE to 0.0215 on industrial 3D datasets by combining flow matching with physics-guided components that target spectral bias and localized shock structures.
FLARE predicts post-cooling displacement fields in directed energy deposition by encoding simulations as implicit neural fields whose weights are regularized to follow an affine structure in parameter space, enabling data-efficient prediction via weight mixing.
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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GeoFunFlow-3D: A Physics-Guided Generative Flow Matching Framework for High-Fidelity 3D Aerodynamic Inference over Complex Geometries
GeoFunFlow-3D reduces pressure-field RRMSE to 0.0215 on industrial 3D datasets by combining flow matching with physics-guided components that target spectral bias and localized shock structures.
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FLARE: A Data-Efficient Surrogate for Predicting Displacement Fields in Directed Energy Deposition
FLARE predicts post-cooling displacement fields in directed energy deposition by encoding simulations as implicit neural fields whose weights are regularized to follow an affine structure in parameter space, enabling data-efficient prediction via weight mixing.