Benchmarking Flow-Based Models for Inverse Design Problems

Inverse design problems are pervasive across science and engineering, including inverse kinematics, materials design, and drug design. Additionally, ill-posed inverse design problems often admit multiple design solutions that produce similar target responses, causing traditional deep learning methods to fail and motivating the development of specialized deep inverse models (DIMs). Recent flow-based models, particularly Flow Matching (FM), have achieved state-of-the-art performance across diverse generative modeling tasks and have recently been applied to specific inverse-design applications, yet their behavior relative to existing DIMs remains poorly characterized. In this work, we systematically benchmark several recent flow-based inverse models, including single-step variants, against competitive DIMs across a diverse collection of publicly available inverse-design benchmarks. We characterize each method by both inference speed and the accuracy of the inverse designs it produces. Our results reveal a pronounced accuracy-speed tradeoff among existing DIMs, with flow-based models frequently achieving competitive performance and occupying portions of the Pareto-optimal frontier. We further find that the relative advantages of different flow-based approaches vary substantially across tasks, highlighting the importance of considering both solution quality and inference cost when selecting inverse models.