MS_118 — Meta-models/surrogate models for uncertainty propagation, sensitivity and reliability analysis (1)

2011 ◽  
pp. 635-635
Keyword(s):  
Reliability Analysis ◽  
Uncertainty Propagation ◽  
Surrogate Models

Adaptive Surrogate Modeling for Multidisciplinary Reliability Analysis Under Time-Dependent Uncertainty

2017 ◽  
Author(s):  
Zhen Hu ◽  
Sankaran Mahadevan
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
Uncertainty Propagation ◽  
Surrogate Modeling ◽  
Surrogate Models ◽  
Time Dependent ◽  
System Reliability Analysis ◽  
Training Point ◽  
Coupling Variables ◽  
Multidisciplinary System

Multidisciplinary systems will remain in transient states when time-dependent interactions are present among the coupling variables. This brings significant challenges to time-dependent multidisciplinary system reliability analysis. This paper develops an adaptive surrogate modeling approach (ASMA) for multidisciplinary system reliability analysis under time-dependent uncertainty. The proposed framework consists of three modules, namely initialization, uncertainty propagation, and three-level global sensitivity analysis (GSA). The first two modules check the quality of the surrogate models and determine when and where we should refine the surrogate models. Approaches are then proposed to estimate the potential error of the failure probability estimate and determine the location of the new training point. In the third module (i.e. three-level GSA), a method is developed to decide which surrogate model to refine, through GSA at three different levels. These three modules are integrated together systematically and enable us to adaptively allocate the computational resources to refine different surrogate models in the system and thus achieve high accuracy and efficiency in time-dependent multidisciplinary system reliability analysis. Results of two numerical examples demonstrate the effectiveness of the proposed framework.

scholarly journals Adaptive Surrogate Modeling for Time-Dependent Multidisciplinary Reliability Analysis

2017 ◽  
Vol 140 (2) ◽  
Author(s):  
Zhen Hu ◽  
Sankaran Mahadevan
Keyword(s):  
Reliability Analysis ◽  
Uncertainty Propagation ◽  
Transient Behavior ◽  
Surrogate Models ◽  
Computational Effort ◽  
Time Step ◽  
Multidisciplinary Analysis ◽  
Coupling Variables ◽  
The Individual ◽  
Computational Resources

Multidisciplinary systems with transient behavior under time-varying inputs and coupling variables pose significant computational challenges in reliability analysis. Surrogate models of individual disciplinary analyses could be used to mitigate the computational effort; however, the accuracy of the surrogate models is of concern, since the errors introduced by the surrogate models accumulate at each time-step of the simulation. This paper develops a framework for adaptive surrogate-based multidisciplinary analysis (MDA) of reliability over time (A-SMART). The proposed framework consists of three modules, namely, initialization, uncertainty propagation, and three-level global sensitivity analysis (GSA). The first two modules check the quality of the surrogate models and determine when and where we should refine the surrogate models from the reliability analysis perspective. Approaches are proposed to estimate the potential error of the failure probability estimate and to determine the locations of new training points. The three-level GSA method identifies the individual surrogate model for refinement. The combination of the three modules facilitates adaptive and efficient allocation of computational resources, and enables high accuracy in the reliability analysis result. The proposed framework is illustrated with two numerical examples.

scholarly journals Modified Dimension Reduction-Based Polynomial Chaos Expansion for Nonstandard Uncertainty Propagation and Its Application in Reliability Analysis

Processes ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1856
Author(s):  
Jeongeun Son ◽  
Yuncheng Du
Keyword(s):  
Dimension Reduction ◽  
Reliability Analysis ◽  
Structural Reliability ◽  
Polynomial Chaos ◽  
Uncertainty Propagation ◽  
Surrogate Models ◽  
Polynomial Chaos Expansion ◽  
Chaos Expansion ◽  
Computationally Efficient ◽  
Standard Distribution

This paper presents an algorithm for efficient uncertainty quantification (UQ) in the presence of many uncertainties that follow a nonstandard distribution (e.g., lognormal). Using the polynomial chaos expansion (PCE), the algorithm builds surrogate models of uncertainty as functions of a standard distribution (e.g., Gaussian variables). The key to build these surrogate models is to calculate PCE coefficients of model outputs, which is computationally challenging, especially when dealing with models defined by complex functions (e.g., nonpolynomial terms) under many uncertainties. To address this issue, an algorithm that integrates the PCE with the generalized dimension reduction method (gDRM) is utilized to convert the high-dimensional integrals, required to calculate the PCE coefficients of model predictions, into several lower-dimensional ones that can be rapidly solved with quadrature rules. The accuracy of the algorithm is validated with four examples in structural reliability analysis and compared to other existing techniques, such as Monte Carlo simulations and the least angle regression-based PCE. Our results show our algorithm provides accurate UQ results and is computationally efficient when dealing with many uncertainties, thus laying the foundation to address UQ in complex control systems.

scholarly journals Deep Neural Network and Polynomial Chaos Expansion-Based Surrogate Models for Sensitivity and Uncertainty Propagation: An Application to a Rockfill Dam

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1830
Author(s):  
Gullnaz Shahzadi ◽  
Azzeddine Soulaïmani
Keyword(s):  
Polynomial Chaos ◽  
Complex Structure ◽  
Uncertainty Propagation ◽  
Surrogate Models ◽  
Polynomial Chaos Expansion ◽  
Soil Parameters ◽  
Chaos Expansion ◽  
Rockfill Dam ◽  
Parametric Studies ◽  
The Impact

Computational modeling plays a significant role in the design of rockfill dams. Various constitutive soil parameters are used to design such models, which often involve high uncertainties due to the complex structure of rockfill dams comprising various zones of different soil parameters. This study performs an uncertainty analysis and a global sensitivity analysis to assess the effect of constitutive soil parameters on the behavior of a rockfill dam. A Finite Element code (Plaxis) is utilized for the structure analysis. A database of the computed displacements at inclinometers installed in the dam is generated and compared to in situ measurements. Surrogate models are significant tools for approximating the relationship between input soil parameters and displacements and thereby reducing the computational costs of parametric studies. Polynomial chaos expansion and deep neural networks are used to build surrogate models to compute the Sobol indices required to identify the impact of soil parameters on dam behavior.

scholarly journals Uncertainty Propagation in Coupled Atmosphere–Wave–Ocean Prediction System: A Study of Hurricane Earl (2010)

2019 ◽  
Vol 147 (1) ◽  
pp. 221-245 ◽  
Author(s):  
Guotu Li ◽  
Milan Curcic ◽  
Mohamed Iskandarani ◽  
Shuyi S. Chen ◽  
Omar M. Knio
Keyword(s):  
Wind Speed ◽  
Initial Conditions ◽  
Uncertainty Propagation ◽  
Storm Track ◽  
Coupled Model ◽  
Surrogate Models ◽  
Maximum Wind Speed ◽  
Track Forecast ◽  
Initial Strength ◽  
Maximum Wind

This study focuses on understanding the evolution of Hurricane Earl (2010) with respect to random perturbations in the storm’s initial strength, size, and asymmetry in wind distribution. We rely on the Unified Wave Interface-Coupled Model (UWIN-CM), a fully coupled atmosphere–wave–ocean system to generate a storm realization ensemble, and use polynomial chaos (PC) expansions to build surrogate models for time evolution of both the maximum wind speed and minimum sea level pressure in Earl. The resulting PC surrogate models provide statistical insights on probability distributions of model responses throughout the simulation time span. Statistical analysis of rapid intensification (RI) suggests that initial perturbations having intensified and counterclockwise-rotated winds are more likely to undergo RI. In addition, for the range of initial conditions considered RI seems mostly sensitive to azimuthally averaged maximum wind speed and asymmetry orientation, rather than storm size and asymmetry magnitude; this is consistent with global sensitivity analysis of PC surrogate models. Finally, we combine initial condition perturbations with a stochastic kinetic energy backscatter scheme (SKEBS) forcing in the UWIN-CM simulations and conclude that the storm tracks are substantially influenced by the SKEBS forcing perturbations, whereas the perturbations in initial conditions alone had only limited impact on the storm-track forecast.

On the Use of Symmetries in Building Surrogate Models

2019 ◽  
Vol 141 (6) ◽  
Author(s):  
M. Giselle Fernández-Godino ◽  
S. Balachandar ◽  
Raphael T. Haftka
Keyword(s):  
Linear Regression ◽  
Uncertainty Propagation ◽  
Computational Cost ◽  
Surrogate Models ◽  
Black Box ◽  
Basis Functions ◽  
Regression Kriging ◽  
Challenges And Opportunities ◽  
Designs Of Experiments ◽  
Low Computational Cost

When simulations are expensive and multiple realizations are necessary, as is the case in uncertainty propagation, statistical inference, and optimization, surrogate models can achieve accurate predictions at low computational cost. In this paper, we explore options for improving the accuracy of a surrogate if the modeled phenomenon presents symmetries. These symmetries allow us to obtain free information and, therefore, the possibility of more accurate predictions. We present an analytical example along with a physical example that has parametric symmetries. Although imposing parametric symmetries in surrogate models seems to be a trivial matter, there is not a single way to do it and, furthermore, the achieved accuracy might vary. We present four different ways of using symmetry in surrogate models. Three of them are straightforward, but the fourth is original and based on an optimization of the subset of points used. The performance of the options was compared with 100 random designs of experiments (DoEs) where symmetries were not imposed. We found that each of the options to include symmetries performed the best in one or more of the studied cases and, in all cases, the errors obtained imposing symmetries were substantially smaller than the worst cases among the 100. We explore the options for using symmetries in two surrogates that present different challenges and opportunities: Kriging and linear regression. Kriging is often used as a black box; therefore, we consider approaches to include the symmetries without changes in the main code. On the other hand, since linear regression is often built by the user; owing to its simplicity, we consider also approaches that modify the linear regression basis functions to impose the symmetries.

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