This framework demonstrates rapid inverse design in nano-devices, suggesting significant efficiency improvements over traditional generative models.
We present a machine-learning framework for nano-device inverse design. Using correlated surrogate annealing, our framework generates designs orders of magnitude faster than traditional generative models, offering broad applicability to general inverse design problems.
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Bezick et al. (2025) studied this question.
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