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Conditioning on adjoint sensitivities can dramatically enhance out-of-distribution generalization in topology optimization, outperforming traditional parameter conditioning methods.
Achieving a staggering 84.2% reduction in prediction error with just 450 samples could revolutionize aerodynamic design processes.
