Adaptive Surrogate Models for Uncertainty Quantification with Partially Observed Information

Mapping Intimacies ◽

10.2514/6.2022-1439 ◽

2022 ◽

Author(s):

Yanwen Xu ◽

Pingfeng Wang

Keyword(s):

Uncertainty Quantification ◽

Surrogate Models ◽

Observed Information ◽

Partially Observed

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Uncertainty Quantification in Surrogate Models Based on Pattern Classification of Cross-validation Errors

12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference ◽

10.2514/6.2012-5437 ◽

2012 ◽

Author(s):

Jie Zhang ◽

Souma Chowdhury ◽

Achille Messac

Keyword(s):

Uncertainty Quantification ◽

Pattern Classification ◽

Cross Validation ◽

Surrogate Models

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A Novel Method Using DS-MCM for Equipment Health Prognosis with Partially Observed Information

Procedia Manufacturing ◽

10.1016/j.promfg.2020.01.205 ◽

2019 ◽

Vol 38 ◽

pp. 1159-1166

Author(s):

Qinming Liu ◽

Ming Dong ◽

F. Frank Chen ◽

Yongpeng Li ◽

Haoxiang Wang

Keyword(s):

Observed Information ◽

Partially Observed ◽

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Assessment of Surrogate Models for Inverse Uncertainty Quantification of Simulant Combustion

AIAA Scitech 2020 Forum ◽

10.2514/6.2020-2137 ◽

2020 ◽

Author(s):

Reetesh Ranjan ◽

Shubham Karpe ◽

Pavan Patel ◽

Suresh Menon

Keyword(s):

Uncertainty Quantification ◽

Surrogate Models

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Efficient Utilization of Surrogate Models for Uncertainty Quantification

PAMM ◽

10.1002/pamm.202000210 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Maria Böttcher ◽

Ferenc Leichsenring ◽

Alexander Fuchs ◽

Wolfgang Graf ◽

Michael Kaliske

Keyword(s):

Uncertainty Quantification ◽

Surrogate Models ◽

Efficient Utilization

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Partially observed information and inference about non-Gaussian mixed linear models

The Annals of Statistics ◽

10.1214/009053605000000543 ◽

2005 ◽

Vol 33 (6) ◽

pp. 2695-2731 ◽

Author(s):

Jiming Jiang

Keyword(s):

Linear Models ◽

Mixed Linear Models ◽

Observed Information ◽

Partially Observed ◽

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The Verification and Uncertainty Quantification of Surrogate Models Used for Structural Analysis

53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference<BR>20th AIAA/ASME/AHS Adaptive Structures Conference<BR>14th AIAA ◽

10.2514/6.2012-1442 ◽

2012 ◽

Author(s):

Michael Ross ◽

Angel Urbina ◽

Todd Simmermacher ◽

Thomas Paez

Keyword(s):

Structural Analysis ◽

Uncertainty Quantification ◽

Surrogate Models

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Comparative study of surrogate models for uncertainty quantification of building energy model: Gaussian Process Emulator vs. Polynomial Chaos Expansion

Energy and Buildings ◽

10.1016/j.enbuild.2016.09.032 ◽

2016 ◽

Vol 133 ◽

pp. 46-58 ◽

Author(s):

Young-Jin Kim

Keyword(s):

Comparative Study ◽

Gaussian Process ◽

Uncertainty Quantification ◽

Polynomial Chaos ◽

Building Energy ◽

Surrogate Models ◽

Polynomial Chaos Expansion ◽

Energy Model ◽

Chaos Expansion ◽

Gaussian Process Emulator

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Uncertainty Quantification in CO2Sequestration Using Surrogate Models from Polynomial Chaos Expansion

Industrial & Engineering Chemistry Research ◽

10.1021/ie300856p ◽

2012 ◽

Vol 52 (9) ◽

pp. 3121-3132 ◽

Author(s):

Yan Zhang ◽

Nikolaos V. Sahinidis

Keyword(s):

Uncertainty Quantification ◽

Polynomial Chaos ◽

Surrogate Models ◽

Polynomial Chaos Expansion ◽

Chaos Expansion

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Physics-informed Machine Learning Method for Forecasting and Uncertainty Quantification of Partially Observed and Unobserved States in Power Grids

Proceedings of the 52nd Hawaii International Conference on System Sciences ◽

10.24251/hicss.2019.416 ◽

2019 ◽

Author(s):

Alexandre Tartakovsky ◽

Ramakrishna Tipireddy

Keyword(s):

Machine Learning ◽

Uncertainty Quantification ◽

Power Grids ◽

Machine Learning Method ◽

Learning Method ◽

Partially Observed

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Mixed Aleatory/Epistemic Uncertainty Quantification for Hypersonic Flows via Gradient-Based Optimization and Surrogate Models

50th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition ◽

10.2514/6.2012-1254 ◽

2012 ◽

Author(s):

Brian Lockwood ◽

Mihai Anitescu ◽

Dimitri Mavriplis

Keyword(s):

Uncertainty Quantification ◽

Epistemic Uncertainty ◽

Surrogate Models ◽

Hypersonic Flows ◽

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