A transformer model with self-attention and auxiliary physics losses learns a direct non-iterative mapping from loads and fields to manufacturable optimized topologies.
Svanberg, The method of moving asymp- totes—a new method for structural optimization, International Journal for Numerical Methods in Engineering 24 (2) (1987) 359–373
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Physics-Informed Transformer for Real-Time High-Fidelity Topology Optimization
A transformer model with self-attention and auxiliary physics losses learns a direct non-iterative mapping from loads and fields to manufacturable optimized topologies.