e-SimFT: Alignment of Generative Models with Simulation Feedback for Pareto-Front Design Exploration

TL;DR

e-SimFT introduces a novel framework that fine-tunes generative models using simulation feedback to effectively explore Pareto fronts in complex engineering design problems.

Contribution

The paper presents the first application of preference alignment methods from LLMs to fine-tune generative models with simulation feedback for Pareto-front exploration.

Findings

e-SimFT produces higher-quality Pareto fronts than existing methods.

Simulation feedback effectively guides generative models in multi-objective design.

Epsilon-sampling improves Pareto front construction in the proposed framework.

Abstract

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 introduce a new framework for Pareto-front design exploration with simulation fine-tuned generative models. First, the framework adopts preference alignment methods developed for Large Language Models (LLMs) and showcases the first application in fine-tuning a generative model for engineering design. The important distinction here is that we use a simulator instead of humans to provide accurate and scalable feedback. Next, we propose 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. Our framework, named e-SimFT, is shown to produce better-quality Pareto fronts than existing multi-objective alignment methods.