Publication | Closed Access
Functional Series Expansions and Quadratic Approximations for Enhancing the Accuracy of the Wiener Path Integral Technique
26
Citations
28
References
2020
Year
Numerical AnalysisPade ApproximantEngineeringNonlinear OscillatorsStochastic SimulationUncertainty QuantificationSystems EngineeringDefinite IntegralModeling And SimulationComputational ElectromagneticsFailure ProbabilitiesApproximation TheoryStatisticsFunctional Series ExpansionsComputer EngineeringNonlinear Signal ProcessingMonte Carlo SamplingQuadratic ApproximationsFunctional Data AnalysisSignal ProcessingStochastic ModelingMonte Carlo MethodJoint Response PdfApproximation Method
A novel Wiener path integral (WPI) technique is developed for determining the response of stochastically excited nonlinear oscillators. This is done by employing functional series expansions in conjunction with quadratic approximations. The technique can be construed as an extension and enhancement in terms of accuracy of the standard (semiclassical) WPI solution approach where only the most probable path connecting initial and final states is considered for determining the joint response probability density function (PDF). In contrast, the technique developed herein accounts also for fluctuations around the most probable path, thus yielding an increased accuracy degree. An additional significant advantage of the proposed enhancement as compared to the most probable path approach relates to the fact that low-probability events (e.g., failure probabilities) can be estimated directly in a computationally efficient manner by determining only a few points of the joint response PDF. Specifically, the normalization step in the standard approach, which requires the evaluation of the joint response PDF over its entire effective domain, is circumvented. The performance of the technique is assessed in several numerical examples pertaining to various oscillators exhibiting diverse nonlinear behaviors, where analytical results are set vis-à-vis pertinent Monte Carlo simulation data.
| Year | Citations | |
|---|---|---|
Page 1
Page 1