Machine Learning and Uncertainty Quantification for Surrogate Models of Integrated Devices With a Large Number of Parameters

IEEE Access ◽

10.1109/access.2018.2888903 ◽

2019 ◽

Vol 7 ◽

pp. 4056-4066 ◽

Cited By ~ 22

Author(s):

Riccardo Trinchero ◽

Mourad Larbi ◽

Hakki M. Torun ◽

Flavio G. Canavero ◽

Madhavan Swaminathan

Keyword(s):

Machine Learning ◽

Uncertainty Quantification ◽

Surrogate Models ◽

Integrated Devices

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Machine learning rbf-based surrogate models for uncertainty quantification of age and time-dependent fracture mechanics

Engineering Fracture Mechanics ◽

10.1016/j.engfracmech.2021.108037 ◽

2021 ◽

pp. 108037

Author(s):

Francisco Evangelista Junior ◽

Iago Freitas de Almeida

Keyword(s):

Machine Learning ◽

Fracture Mechanics ◽

Uncertainty Quantification ◽

Surrogate Models ◽

Time Dependent

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Uncertainty quantification of ultimate compressive strength of CCFST columns using hybrid machine learning model

Engineering With Computers ◽

10.1007/s00366-021-01339-1 ◽

2021 ◽

Author(s):

Mai-Suong T. Nguyen ◽

Minh-Chien Trinh ◽

Seung-Eock Kim

Keyword(s):

Machine Learning ◽

Compressive Strength ◽

Uncertainty Quantification ◽

Learning Model ◽

Ultimate Compressive Strength ◽

Machine Learning Model ◽

Hybrid Machine

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Enabling forward uncertainty quantification and sensitivity analysis in cardiac electrophysiology by reduced order modeling and machine learning

International Journal for Numerical Methods in Biomedical Engineering ◽

10.1002/cnm.3450 ◽

2021 ◽

Author(s):

Stefano Pagani ◽

Andrea Manzoni

Keyword(s):

Machine Learning ◽

Sensitivity Analysis ◽

Uncertainty Quantification ◽

Cardiac Electrophysiology ◽

Reduced Order Modeling ◽

Reduced Order

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Machine learning based algorithms for uncertainty quantification in numerical weather prediction models

Journal of Computational Science ◽

10.1016/j.jocs.2020.101295 ◽

2021 ◽

Vol 50 ◽

pp. 101295

Author(s):

Azam Moosavi ◽

Vishwas Rao ◽

Adrian Sandu

Keyword(s):

Machine Learning ◽

Uncertainty Quantification ◽

Numerical Weather Prediction ◽

Prediction Models ◽

Weather Prediction ◽

Numerical Weather ◽

Numerical Weather Prediction Models

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Multi-fidelity machine-learning with uncertainty quantification and Bayesian optimization for materials design: Application to ternary random alloys

The Journal of Chemical Physics ◽

10.1063/5.0015672 ◽

2020 ◽

Vol 153 (7) ◽

pp. 074705 ◽

Cited By ~ 3

Author(s):

Anh Tran ◽

Julien Tranchida ◽

Tim Wildey ◽

Aidan P. Thompson

Keyword(s):

Machine Learning ◽

Uncertainty Quantification ◽

Materials Design ◽

Bayesian Optimization ◽

Random Alloys

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Uncertainty quantification of machine learning models: on conformal prediction

10.1117/12.2600838 ◽

2021 ◽

Author(s):

Inimfon I. Akpabio ◽

Serap A. Savari

Keyword(s):

Machine Learning ◽

Uncertainty Quantification ◽

Learning Models ◽

Conformal Prediction ◽

Machine Learning Models

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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 ◽

Cited By ~ 3

Author(s):

Jie Zhang ◽

Souma Chowdhury ◽

Achille Messac

Keyword(s):

Uncertainty Quantification ◽

Pattern Classification ◽

Cross Validation ◽

Surrogate Models

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Comparison of Group-Contribution and Machine Learning-based Property Prediction Models with Uncertainty Quantification

31st European Symposium on Computer Aided Process Engineering - Computer Aided Chemical Engineering ◽

10.1016/b978-0-323-88506-5.50118-2 ◽

2021 ◽

pp. 755-760

Author(s):

Adem R.N. Aouichaoui ◽

Resul Al ◽

Jens Abildskov ◽

Gürkan Sin

Keyword(s):

Machine Learning ◽

Uncertainty Quantification ◽

Prediction Models ◽

Group Contribution ◽

Property Prediction

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Special Issue on Uncertainty Quantification, Machine Learning, and Data-Driven Modeling of Biological Systems

Computer Methods in Applied Mechanics and Engineering ◽

10.1016/j.cma.2020.112832 ◽

2020 ◽

Vol 362 ◽

pp. 112832

Author(s):

Adrian Buganza Tepole ◽

David Nordsletten ◽

Krishna Garikipati ◽

Ellen Kuhl

Keyword(s):

Machine Learning ◽

Uncertainty Quantification ◽

Biological Systems ◽

Data Driven ◽

Special Issue ◽

Data Driven Modeling

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Machine-Learning-Based Hybrid Random-Fuzzy Uncertainty Quantification for EMC and SI Assessment

IEEE Transactions on Electromagnetic Compatibility ◽

10.1109/temc.2020.2980790 ◽

2020 ◽

Vol 62 (6) ◽

pp. 2538-2546 ◽

Cited By ~ 1

Author(s):

Simon De Ridder ◽

Domenico Spina ◽

Nicola Toscani ◽

Flavia Grassi ◽

Dries Vande Ginste ◽

...

Keyword(s):

Machine Learning ◽

Uncertainty Quantification ◽

Fuzzy Uncertainty

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