scholarly journals Limits of variance-based sensitivity analysis for non-identifiability testing in high dimensional dynamic models

Automatica ◽  
2012 ◽  
Vol 48 (11) ◽  
pp. 2740-2749 ◽  
Author(s):  
Simona Dobre ◽  
Thierry Bastogne ◽  
Christophe Profeta ◽  
Muriel Barberi-Heyob ◽  
Alain Richard
Keyword(s):  
Sensitivity Analysis ◽  
Dynamic Models ◽  
High Dimensional

scholarly journals Gaussian Processes Proxy Model with Latent Variable Models and Variogram-Based Sensitivity Analysis for Assisted History Matching

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4290
Author(s):  
Dongmei Zhang ◽  
Yuyang Zhang ◽  
Bohou Jiang ◽  
Xinwei Jiang ◽  
Zhijiang Kang
Keyword(s):  
Sensitivity Analysis ◽  
Gaussian Processes ◽  
Latent Variable ◽  
History Matching ◽  
Latent Variable Models ◽  
High Dimensional ◽  
Model Parameters ◽  
Variable Model ◽  
Assisted History Matching ◽  
Proxy Models

Reservoir history matching is a well-known inverse problem for production prediction where enormous uncertain reservoir parameters of a reservoir numerical model are optimized by minimizing the misfit between the simulated and history production data. Gaussian Process (GP) has shown promising performance for assisted history matching due to the efficient nonparametric and nonlinear model with few model parameters to be tuned automatically. Recently introduced Gaussian Processes proxy models and Variogram Analysis of Response Surface-based sensitivity analysis (GP-VARS) uses forward and inverse Gaussian Processes (GP) based proxy models with the VARS-based sensitivity analysis to optimize the high-dimensional reservoir parameters. However, the inverse GP solution (GPIS) in GP-VARS are unsatisfactory especially for enormous reservoir parameters where the mapping from low-dimensional misfits to high-dimensional uncertain reservoir parameters could be poorly modeled by GP. To improve the performance of GP-VARS, in this paper we propose the Gaussian Processes proxy models with Latent Variable Models and VARS-based sensitivity analysis (GPLVM-VARS) where Gaussian Processes Latent Variable Model (GPLVM)-based inverse solution (GPLVMIS) instead of GP-based GPIS is provided with the inputs and outputs of GPIS reversed. The experimental results demonstrate the effectiveness of the proposed GPLVM-VARS in terms of accuracy and complexity. The source code of the proposed GPLVM-VARS is available at https://github.com/XinweiJiang/GPLVM-VARS.

Effective Global Sensitivity Analysis for High-Dimensional Hydrologic and Water Quality Models

2019 ◽  
Vol 24 (1) ◽  
pp. 04018057 ◽  
Author(s):  
Yogesh Khare ◽  
Christopher J. Martinez ◽  
Rafael Muñoz-Carpena ◽  
Adelbert “Del” Bottcher ◽  
Andrew James
Keyword(s):  
Water Quality ◽  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
High Dimensional ◽  
Quality Models ◽  
Global Sensitivity ◽  
Water Quality Models

scholarly journals Accelerated Sensitivity Analysis in High-Dimensional Stochastic Reaction Networks

PLoS ONE ◽  
2015 ◽  
Vol 10 (7) ◽  
pp. e0130825 ◽  
Author(s):  
Georgios Arampatzis ◽  
Markos A. Katsoulakis ◽  
Yannis Pantazis
Keyword(s):  
Sensitivity Analysis ◽  
Reaction Networks ◽  
High Dimensional ◽  
Stochastic Reaction ◽  
Stochastic Reaction Networks

scholarly journals Advanced Performance Metrics and Sensitivity Analysis for Model Validation and Calibration

2020 ◽  
Author(s):  
Urmila Agrawal ◽  
Pavel Etingov ◽  
Renke Huang
Keyword(s):  
Sensitivity Analysis ◽  
Power Systems ◽  
Model Validation ◽  
Real World ◽  
Model Calibration ◽  
Dynamic Models ◽  
Performance Metrics ◽  
Step Response ◽  
Response Characteristics ◽  
Oscillatory Response

<pre>High quality generator dynamic models are critical to reliable and accurate power systems studies and planning. With the availability of PMU measurements, measurement-based approach for model validation has gained significant prominence. Currently, the model validation results are analyzed by visually comparing real--world PMU measurements with the model-based response measurements, and parameter adjustments rely mostly on engineering experience. This paper proposes advanced performance metrics to systematically quantify the generator dynamic model validation results by separately taking into consideration slow governor response and comparatively fast oscillatory response. The performance metric for governor response is based on the step response characteristics of a system and the metric for oscillatory response is based on the response of generator to each system mode calculated using modal analysis. The proposed metrics in this paper is aimed at providing critical information to help with the selection of parameters to be tuned for model calibration by performing enhanced sensitivity analysis, and also help with rule-based model calibration. Results obtained using both simulated and real-world measurements validate the effectiveness of the proposed performance metrics and sensitivity analysis for model validation and calibration.</pre>

scholarly journals Derivative-Based Global Sensitivity Analysis for Models with High-Dimensional Inputs and Functional Outputs

2019 ◽  
Vol 41 (6) ◽  
pp. A3524-A3551 ◽  
Author(s):  
Helen L. Cleaves ◽  
Alen Alexanderian ◽  
Hayley Guy ◽  
Ralph C. Smith ◽  
Meilin Yu
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
High Dimensional ◽  
Global Sensitivity
Sign in / Sign up

Export Citation Format

Share Document