Surrogate modelling for stochastic dynamical systems by combining NARX models and polynomial chaos expansions

C. V. Mai (1) , M. D. Spiridonakos (2) , E. N. Chatzi (2) , B. Sudret (3, 1)

1 Chair of Risk, Safety and Uncertainty Quantification [ETH Zurich]
2 Chair of Structural Mechanics
3 GdR MASCOT-NUM - Méthodes d'Analyse Stochastique des Codes et Traitements Numériques

The application of polynomial chaos expansions (PCEs) to the propagation of uncertainties in stochastic dynamical models is well-known to face challenging issues. The accuracy of PCEs degenerates quickly in time. Thus maintaining a sufficient level of long term accuracy requires the use of high-order polynomials. In numerous cases, it is even infeasible to obtain accurate metamodels with regular PCEs due to the fact that PCEs cannot represent the dynamics. To overcome the problem, an original numerical approach was recently proposed that combines PCEs and non-linear autoregressive with exogenous input (NARX) models, which are a universal tool in the field of system identification. The approach relies on using NARX models to mimic the dynamical behaviour of the system and dealing with the uncertainties using PCEs. The PC-NARX model was built by means of heuristic genetic algorithms. This paper aims at introducing the least angle regression (LAR) technique for computing PC-NARX models, which consists in solving two linear regression problems. The proposed approach is validated with structural mechanics case studies, in which uncertainties arising from both structures and excitations are taken into account. Comparison with Monte Carlo simulation and regular PCEs is also carried out to demonstrate the effectiveness of the proposed approach.

Mots clés: surrogate models; polynomial chaos expansions; nonlinear autoregressive with exogenous input (NARX) models; Monte Carlo simulation; dynamical systems

Domaines: Méthodologie [stat.ME]; Calcul [stat.CO]

C. V. Mai, M. D. Spiridonakos, E. N. Chatzi, B. Sudret. Surrogate modelling for stochastic dynamical systems by combining NARX models and polynomial chaos expansions. 2017. ⟨hal-01429009⟩