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.
NeurIPS 2024 Workshop on Data-driven and Differentiable Simulations, Surrogates, and Solvers (D3S3) , year=
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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.