Sequential sampling strategy for extreme event statistics in nonlinear dynamical systems
Abstract
Understanding the statistics of extreme events in dynamical systems of high complexity is of vital importance for reliability assessment and design. We formulate a method to pick samples optimally so that we have rapid convergence of the full statistics of a quantity of interest, including the tails that describe extreme events. This is important for large-scale problems in science and engineering, where we desire to predict the statistics of relevant quantities but can only afford a limited number of simulations or experiments due to their very expensive cost. We demonstrate our approach in a hydromechanical system with millions of degrees of freedom, where only 10–20 carefully selected samples can lead to accurate approximation of the extreme event statistics.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 16, 2018
- Source ID
- 10.1073/pnas.1813263115
Entities
People
- Mustafa A Mohamad
- Themistoklis Sapsis
Organizations
- Air Force Office of Scientific Research
- Army Research Office
- Massachusetts Institute of Technology
- Office of Naval Research