KATOsuper presents an objective-agnostic framework that couples neural-reparameterized topology optimization with sensitivity-consistent Fourier neural operators, addressing gradient inconsistency between predicted objectives and sensitivities for stable structural optimization.
See LICENSE fileTopology OptimizationFourier Neural OperatorSensitivity Analysis
A conditional diffusion approach to structural topology optimization from MIT that uses surrogate-model guidance to optimise both mechanical compliance and manufacturability, outperforming conditional GANs by a wide margin.
arXiv preprint (code and data publicly available)Topology OptimizationDiffusion ModelGenerative Design
Proposes a ROM-based preconditioner to accelerate topology optimization, reducing computational cost of high-fidelity finite element analysis in structural conceptual design.
See LICENSE fileTopology OptimizationReduced Order ModelingPreconditioner
