Simulation-based inference (SBI) methods tackle complex scientific models with challenging inverse problems. However, SBI models often face a significant hurdle due to their non-differentiable nature, which hampers the use of gradient-based optimization techniques. Bayesian Optimal Experimental Design (BOED) is a powerful approach that aims to make the most efficient use of experimental resources for improved inferences. While stochastic gradient BOED methods have shown promising results in high-dimensional design problems, they have mostly neglected the integration of BOED with SBI due to the difficult non-differentiable property of many SBI simulators. In this work, we establish a crucial connection between ratio-based SBI inference algorithms and stochastic gradient-based variational inference by leveraging mutual information bounds. This connection allows us to extend BOED to SBI applications, enabling the simultaneous optimization of experimental designs and amortized inference functions. We demonstrate our approach on a simple linear model and offer implementation details for practitioners.

翻译：模拟推断方法用于处理具有挑战性逆问题的复杂科学模型。然而，模拟推断模型常因不可微性质而面临重大障碍，这阻碍了基于梯度的优化技术的应用。贝叶斯最优实验设计是一种旨在高效利用实验资源以改善推断效果的强大方法。尽管基于随机梯度的贝叶斯最优实验设计方法在高维设计问题中展现出令人瞩目的成果，但由于许多模拟推断模拟器的不可微特性，这类方法大多忽略了将贝叶斯最优实验设计与模拟推断相结合。在本工作中，我们通过利用互信息界限，在基于比率模拟推断算法与基于随机梯度的变分推断之间建立了关键联系。这一联系使我们能够将贝叶斯最优实验设计扩展至模拟推断应用，从而实现实验设计与摊销推断函数的同步优化。我们在线性简单模型上演示了该方法，并为实践者提供了实现细节。