Scalable Inference for Rare Events: Computational Methods for Estimating Probability of Tail Events
Abstract
This report presents key findings and results in the Scalable Inference for Rare Events (SIRE) project (FA8650-16-C-7646). Under the project, we discovered deep theoretical connections between Koopman operator theory and rare event simulation in stochastic differential equations. We then developed a generalized approach for constructing efficient importance sampling methods for linear stochastic differential equations using the Kolmogorov Backward (Ornstein-Uhlenbeck) operator. We show that this approach is a special case of the Koopman operator approach. Additionally, we constructed rotorcraft models that capture critical stall phenomena that was used for computation. We then demonstrate large deviations-based importance sampling and splitting methods on rotorcraft and electrical models.
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 15, 2019
- Accession Number
- AD1090887
Entities
People
- Quan Long
- Tuhin Sahai
- Yibin B. Zhang
- Youssef Marzouk
Organizations
- United Technologies Research Center