基于先验摊销神经后验估计的快速可靠概率反射测量反演
Fast and Reliable Probabilistic Reflectometry Inversion with Prior-Amortized Neural Posterior Estimation
AI总结：
针对反射测量反演计算成本高且仅得单一解的不可靠问题，提出概率深度学习方法PANPE，结合基于模拟的推断与自适应先验，在几秒内识别所有现实结构，支持高通量表征等场景，为反问题提供快速可靠的推断。
摘要：
从散射X射线或中子的测量中重建薄膜和多层结构是物理学、化学和生物学进步的关键。然而，寻找与反射测量数据兼容的所有结构对标准算法而言在计算上是不可行的，这通常导致不可靠的分析，仅识别出单一潜在解。我们通过一种概率深度学习方法解决了这种可靠性缺失问题，该方法能在几秒钟内识别出所有现实结构，为反射测量设定了新标准。我们的方法，即先验摊销神经后验估计（PANPE），将基于模拟的推断与新颖的自适应先验相结合，这些先验将已知结构属性和可控实验条件告知推断网络。PANPE网络支持高通量样品表征、演化结构实时监测或多个实验数据集协同精修等关键场景，并可被改造以在许多其他反问题中提供快速、可靠且灵活的推断。
英文摘要：
Reconstructing the structure of thin films and multilayers from measurements of scattered X-rays or neutrons is key to progress in physics, chemistry, and biology. However, finding all structures compatible with reflectometry data is computationally prohibitive for standard algorithms, which typically results in unreliable analysis with only a single potential solution identified. We address this lack of reliability with a probabilistic deep learning method that identifies all realistic structures in seconds, setting new standards in reflectometry. Our method, Prior-Amortized Neural Posterior Estimation (PANPE), combines simulation-based inference with novel adaptive priors that inform the inference network about known structural properties and controllable experimental conditions. PANPE networks support key scenarios such as high-throughput sample characterization, real-time monitoring of evolving structures, or the co-refinement of several experimental data sets, and can be adapted to provide fast, reliable, and flexible inference across many other inverse problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
