Computer simulations have long presented the exciting possibility of scientific insight into complex real-world processes. Despite the power of modern computing, however, it remains challenging to systematically perform inference under simulation models. This has led to the rise of simulation-based inference (SBI), a class of machine learning-enabled techniques for approaching inverse problems with stochastic simulators. Many such methods, however, require large numbers of simulation samples and face difficulty scaling to high-dimensional settings, often making inference prohibitive under resource-intensive simulators. To mitigate these drawbacks, we introduce active sequential neural posterior estimation (ASNPE). ASNPE brings an active learning scheme into the inference loop to estimate the utility of simulation parameter candidates to the underlying probabilistic model. The proposed acquisition scheme is easily integrated into existing posterior estimation pipelines, allowing for improved sample efficiency with low computational overhead. We further demonstrate the effectiveness of the proposed method in the travel demand calibration setting, a high-dimensional inverse problem commonly requiring computationally expensive traffic simulators. Our method outperforms well-tuned benchmarks and state-of-the-art posterior estimation methods on a large-scale real-world traffic network, as well as demonstrates a performance advantage over non-active counterparts on a suite of SBI benchmark environments.

翻译：长期以来，计算机模拟为理解复杂的现实世界过程提供了令人兴奋的科学洞见可能性。然而，尽管现代计算能力强大，在模拟模型下系统地进行推理仍然具有挑战性。这催生了基于模拟的推理（SBI）的兴起，这是一类利用机器学习技术处理具有随机模拟器的逆问题的方法。然而，许多此类方法需要大量模拟样本，且难以扩展到高维设置，在资源密集型模拟器下进行推理往往变得不可行。为缓解这些缺陷，我们提出了主动序列神经后验估计（ASNPE）。ASNPE将主动学习方案引入推理循环，以评估模拟参数候选对底层概率模型的效用。所提出的获取方案可轻松集成到现有的后验估计流程中，从而以较低的计算开销实现改进的样本效率。我们进一步证明了所提方法在出行需求标定场景中的有效性，这是一个通常需要计算成本高昂的交通模拟器的高维逆问题。我们的方法在大型现实世界交通网络上优于经过良好调优的基准方法和最先进的后验估计方法，并且在一系列SBI基准环境中展现出优于非主动方法的性能优势。