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GANTL: Towards Practical and Real-Time Topology Optimization with Conditional GANs and Transfer Learning
We present a deep learning method based on generative adversarial networks for generative design exploration.
Real-Time Topology Optimization in 3D via Deep Transfer Learning
The published literature on topology optimization has exploded over the last two decades to include methods that use shape and topological derivatives or evolutionary algorithms formulated on various geometric representations and parametrizations.
Shape Complementarity Analysis for Objects of Arbitrary Shape
The basic problem of shape complementarity analysis appears fundamental to applications as diverse as mechanical design, assembly automation, robot motion planning, micro- and nano-fabrication, protein-ligand binding, and rational drug design.
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