Neural posterior estimation methods based on discrete normalizing flows have become established tools for simulation-based inference (SBI), but scaling them to high-dimensional problems can be challenging. Building on recent advances in generative modeling, we here present flow matching posterior estimation (FMPE), a technique for SBI using continuous normalizing flows. Like diffusion models, and in contrast to discrete flows, flow matching allows for unconstrained architectures, providing enhanced flexibility for complex data modalities. Flow matching, therefore, enables exact density evaluation, fast training, and seamless scalability to large architectures--making it ideal for SBI. We show that FMPE achieves competitive performance on an established SBI benchmark, and then demonstrate its improved scalability on a challenging scientific problem: for gravitational-wave inference, FMPE outperforms methods based on comparable discrete flows, reducing training time by 30% with substantially improved accuracy. Our work underscores the potential of FMPE to enhance performance in challenging inference scenarios, thereby paving the way for more advanced applications to scientific problems.

翻译：基于离散归一化流的神经后验估计方法已成为模拟推断的标准工具，但在高维问题中扩展这些方法仍面临挑战。基于生成建模领域的最新进展，本文提出流匹配后验估计（FMPE）——一种利用连续归一化流进行模拟推断的技术。与扩散模型类似，且不同于离散流，流匹配允许采用无约束架构，为复杂数据模态提供了更强的灵活性。因此，流匹配能够实现精确密度评估、快速训练，并平滑扩展至大型架构——这使其成为模拟推断的理想工具。我们证明，FMPE在标准模拟推断基准测试中展现出竞争性性能，并在具有挑战性的科学问题（引力波推断）中验证了其改进的可扩展性：相比基于同类离散流的方法，FMPE的训练时间减少30%，精度显著提升。本工作揭示了FMPE在复杂推断场景中增强性能的潜力，为科学问题的高级应用奠定了基础。