首页|A neural network solution of first-passage problems
A neural network solution of first-passage problems
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
万方数据
维普
This paper proposes a novel method for solving the first-passage time probability problem of nonlinear stochastic dynamic systems.The safe domain boundary is exactly imposed into the radial basis function neural network(RBF-NN)architecture such that the solution is an admissible function of the boundary-value problem.In this way,the neural network solution can automatically satisfy the safe domain boundaries and no longer requires adding the corresponding loss terms,thus efficiently handling structure failure problems defined by various safe domain boundaries.The effectiveness of the proposed method is demonstrated through three nonlinear stochastic examples defined by different safe domains,and the results are validated against the extensive Monte Carlo simulations(MCSs).
first-passage time probabilitynonlinear stochastic dynamic systemradial basis function neural network(RBF-NN)safe domain boundaryMonte Carlo simulation(MCS)
Jiamin QIAN、Lincong CHEN、J.Q.SUN
展开 >
College of Civil Engineering,Huaqiao University,Xiamen 361021,Fujian Province,China
Department of Mechanical Engineering,University of California,Merced,CA 95343,U.S.A.
NETL
NSTL
万方数据
维普
