# GenPFN

Engineering design often has only 10 to 1,000 evaluated designs because simulations and experiments are costly. GenPFN trains no new generator: a frozen TabPFN classifier reads labeled designs, candidates are interpolated between desired designs and updated with classifier gradients and Gaussian noise, and every candidate is evaluated by the problem's own constraints and objectives. Across fourteen engineering problems, GenPFN v1 has higher class satisfaction and lower realism distance than the tested CTGAN, TVAE and TabDDPM; in the stern case, 993 of 1,000 candidates passed geometric checks and met the propeller-placement area requirement, versus 28 of 2,000 random context designs.

Engineering design often has only 10 to 1,000 evaluated designs because simulations and experiments are costly. GenPFN trains no new generator: a frozen TabPFN classifier reads labeled designs, candidates are interpolated between desired designs and updated with classifier gradients and Gaussian noise, and every candidate is evaluated by the problem's own constraints and objectives. Across fourteen engineering problems, GenPFN v1 has higher class satisfaction and lower realism distance than the tested CTGAN, TVAE and TabDDPM; in the stern case, 993 of 1,000 candidates passed geometric checks and met the propeller-placement area requirement, versus 28 of 2,000 random context designs.