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A novel non-intrusive ROM for randomly excited linear dynamical systems with high stochastic dimension using ANN

Bharti, C and Ghosh, D (2024) A novel non-intrusive ROM for randomly excited linear dynamical systems with high stochastic dimension using ANN. In: Probabilistic Engineering Mechanics, 75 .

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Official URL: https://doi.org/10.1016/j.probengmech.2023.103570


Analyzing large stochastic dynamical systems is computationally very expensive. A statistical simulation framework requires invoking the solver multiple times � ranging from thousands to millions. A non-intrusive reduced order model (ROM) serves as a computationally efficient alternative in this framework. Uncertainties in dynamical systems originate from two sources: system parameters and excitation. However, all the existing ROMs have been developed for uncertainty only in the system parameters. To the best of the authors� knowledge, no ROM exists for dynamical systems under random excitation. A discretization of the input process increases the stochastic dimensionality, typically by a few hundred or thousand. This leads to a high dimensional interpolation or regression in the ROM, which is practically infeasible. This issue is addressed in this work by proposing a novel non-intrusive ROM that bypasses the need for such discretization. Accordingly, a regression is carried out directly on the random excitation using a neural network-based surrogate model. A principal component-based data compression is used in tandem to reduce the stochastic dimensionality of excitations. Detailed numerical studies are conducted to study the accuracy and efficiency of the proposed ROM using two examples: a mistuned bladed disk problem and a soil�structure interaction. The numerical results show that the proposed ROM is accurate and gains a significant speed-up of more than sixty for both examples. Using the proposed ROM, the cost of uncertainty quantification can be reduced significantly within the framework of Monte Carlo simulation. © 2023

Item Type: Journal Article
Publication: Probabilistic Engineering Mechanics
Publisher: Elsevier Ltd
Additional Information: The copyright for this article belongs to Elsevier Ltd.
Keywords: Intelligent systems; Linear control systems; Monte Carlo methods; Neural networks; Regression analysis; Stochastic systems; Uncertainty analysis, High-dimension regression; Higher dimensions; Neural-networks; Non-intrusive; Non-intrusive reduced order model; Random excitations; Reduced order modelling; Reduced-order model; Soil-structure interaction; Uncertainty quantifications, Dynamical systems
Department/Centre: Division of Mechanical Sciences > Civil Engineering
Date Deposited: 01 Mar 2024 09:47
Last Modified: 01 Mar 2024 09:47
URI: https://eprints.iisc.ac.in/id/eprint/84003

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