Increased deployment of autonomous systems in fields like transportation and robotics have seen a corresponding increase in safety-critical failures. These failures can be difficult to model and debug due to the relative lack of data: compared to tens of thousands of examples from normal operations, we may have only seconds of data leading up to the failure. This scarcity makes it challenging to train generative models of rare failure events, as existing methods risk either overfitting to noise in the limited failure dataset or underfitting due to an overly strong prior. We address this challenge with CalNF, or calibrated normalizing flows, a self-regularized framework for posterior learning from limited data. CalNF achieves state-of-the-art performance on data-limited failure modeling and inverse problems and enables a first-of-a-kind case study into the root causes of the 2022 Southwest Airlines scheduling crisis.

翻译：自动驾驶系统在交通和机器人等领域的广泛应用伴随着安全关键故障的相应增加。由于相关数据的相对缺乏，这些故障往往难以建模和调试：与正常运行状态下的数万条数据相比，我们可能仅拥有故障发生前数秒的数据。这种稀缺性使得罕见故障事件的生成模型训练极具挑战，因为现有方法既可能因对有限故障数据集中的噪声过拟合而失效，也可能因先验约束过强导致欠拟合。为此，我们提出CalNF（校准归一化流）——一种基于自正则化的有限数据后验学习框架。CalNF在数据受限的故障建模与反问题上实现了最先进的性能，并首次实现了对2022年西南航空调度危机根本原因的案例研究。