Uncovering Extreme Event Mechanisms for Prediction and Control with Sensitivity-Balanced Projections
arXiv:2606.05618v1 Announce Type: cross Abstract: 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 p
The increasing availability of complex data sets and advancements in AI/ML techniques are making it possible to tackle long-standing problems like extreme event prediction in chaotic systems.
Predicting and controlling extreme events, from natural disasters to system failures, has significant implications for global stability, economic resilience, and risk management.
The development of interpretable AI techniques moves beyond black-box models to offer actionable insights into the 'why' behind extreme events, enabling more effective prevention and mitigation strategies.
- · Insurance and reinsurance industry
- · Disaster preparedness agencies
- · Energy grid operators
- · Financial systems
- · Entities reliant on reactive crisis management
- · Sectors vulnerable to unpredicted systemic shocks
Improved prediction of extreme events in complex systems like weather, seismology, and potentially financial markets.
Reduced economic losses and enhanced safety due to proactive intervention and control mechanisms for these events.
The application of these interpretable AI methods could extend to other complex societal challenges, fostering greater resilience and stability.
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Read at arXiv cs.LG