e-SimFT: Pareto-Optimal Sampling of Generative Design Models Fine-tuned with Simulation Feedback

Deep generative models have recently shown success in solving complex engineering design problems where models predict solutions that address the design requirements specified as input. However, there remains a challenge in aligning such models for effective design exploration. For many design problems, finding a solution that meets all the requirements is infeasible. In such a case, engineers prefer to obtain a set of Pareto-optimal solutions with respect to those requirements, but uniform sampling of generative models may not yield a useful Pareto front. To address this gap, we first fine-tune generative models with simulation feedback, and then apply epsilon-sampling, inspired by the epsilon-constraint method used for Pareto front generation with classical optimization algorithms, to construct a high-quality Pareto front with the fine-tuned models.