Efficient estimation of first passage probability of high-dimensional nonlinear systems

An efficient method for estimating low first passage probabilities of high-dimensional nonlinear systems based on asymptotic estimation of low probabilities is presented. The method does not require any a priori knowledge of the system, i.e. it is a black-box method, and has very low requirements on the system memory. Consequently, high-dimensional problems can be handled, and nonlinearities in the model neither bring any difficulty in applying it nor lead to considerable reduction of its efficiency. These characteristics suggest that the method is a powerful candidate for complicated problems. First, the failure probabilities of three well-known nonlinear systems are estimated. Next, a reduced degree-of-freedom model of a wind turbine is developed and is exposed to a turbulent wind field. The model incorporates very high dimensions and strong nonlinearities simultaneously. The failure probability of the wind turbine model is estimated down to very low values; this demonstrates the efficiency and power of the method on a realistic high-dimensional highly nonlinear system.