Simulation-based inference (SBI) is transforming experimental sciences by enabling parameter estimation in complex non-linear models from simulated data. A persistent challenge, however, is model misspecification. In a Bayesian setting, targeting posterior distributions, errors may arise from the simulator, the noise or prior modelling. These model components are only approximations of reality, and severe mismatches can yield biased or overconfident posteriors. We address this issue by introducing Flow Matching Corrected Posterior Estimation (FMCPE), a framework that leverages the flow matching paradigm to refine simulation-trained posterior estimators using a small set of calibration samples. Our approach proceeds in two stages: first, a posterior approximator is trained on abundant simulated data; second, flow matching transports its predictions toward the true posterior supported by calibration observations. We rely on the later to guide the correction, without requiring explicit knowledge of the misspecification form or of which model components are affected. This design enables FMCPE to combine the scalability of SBI with robustness to distributional shift. Across synthetic benchmarks and real-world datasets, we show that our proposal consistently mitigates the effects of misspecification, delivering improved inference accuracy and uncertainty quantification compared to standard SBI baselines, while remaining computationally efficient.

翻译：仿真推断（SBI）通过模拟数据实现复杂非线性模型中的参数估计，正在变革实验科学领域。然而，模型设定偏误始终是其面临的核心挑战。在贝叶斯框架下进行后验分布推断时，误差可能来源于仿真器、噪声建模或先验建模。这些模型组分仅是现实情况的近似表达，严重的模型失配会导致有偏或过度自信的后验估计。针对该问题，我们提出流匹配校正后验估计（FMCPE）框架，该框架利用流匹配范式通过少量校准样本对基于仿真的后验估计器进行精化。本方法分两阶段进行：首先，在大量模拟数据上训练后验近似器；其次，通过流匹配将其预测结果向校准观测数据支撑的真实后验分布迁移。我们依赖校准数据引导修正过程，无需显式了解模型设定偏误的形式或受影响的模型组分。该设计使FMCPE兼具SBI的可扩展性与对分布偏移的鲁棒性。合成基准测试与真实数据集实验表明，与标准SBI基线方法相比，本方法能持续缓解模型设定偏误的影响，在保持计算效率的同时，显著提升推断精度与不确定性量化质量。