Zoom link: https://upenn.zoom.us/j/98220304722

Rare and extreme events, such as natural disasters, cascading failures, and accidents in autonomous systems, can have severe consequences but are inherently data-scarce: precisely because they occur infrequently, there are often too few observations to reliably learn their statistics directly from data. This creates a fundamental challenge for data-driven prediction and decision-making, particularly when the underlying systems involve high-dimensional uncertainty and expensive physical models. In this talk, I will present a computational framework based on large deviation theory (LDT) for rare-event estimation and control. LDT connects the probability of a rare event to a deterministic optimization problem over the uncertain parameters, whose solution identifies the most likely mechanism leading to the event. Building on this connection, I will discuss sampling-free approximations for rare-event probabilities, LDT-informed importance sampling algorithms, and optimization under rare chance constraints. These approaches exploit the geometry of the rare-event set and information from the associated optimization problem to substantially reduce the computational cost of both probability estimation and risk-aware control.

I will illustrate these methods through applications to physical and engineered systems, with a particular focus on traffic systems involving autonomous vehicles. These developments form part of RareDT, a broader effort to develop digital twins that go beyond predicting typical system behavior and instead incorporate the quantification and control of rare but consequential events.