Uncovering Extreme Event Mechanisms for Prediction and Control with Sensitivity-Balanced Projections
Extreme events -- such as earthquakes and coronal mass ejections -- are common in many chaotic dynamical systems, yet are difficult to characterize and predict due to the subtle instability mechanisms that drive them. In this work, we develop an interpretable technique that reveals the underlying mechanisms behind extreme events and uses them to build data-driven forecasts and intuitive event suppression controllers. In particular, we utilize the covariance balancing reduction using adjoint snapshots (CoBRAS) method to identify linear oblique projections that best capture the sensitivity of a quantity of interest and reconstruct the original state. Importantly, we bypass the need for cumb...
| Authors | Nicholas Zolman, Sajeda Mokbel, Samuel E. Otto et al. |
|---|---|
| Primary category | nlin.CD |
| Published | 4 June 2026 |
| Last updated at source | 4 June 2026 |
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