Gaussian process regression + deep neural network autoencoder for probabilistic surrogate modeling in nonlinear mechanics of solids

DESHPANDE, Saurabh; RAPPEL, Hussein; Hobbs, Mark; BORDAS, Stéphane; Lengiewicz, Jakub

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DESHPANDE, Saurabh; RAPPEL, Hussein; Hobbs, Mark et al.

2024

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Keywords :

Deep Neural Networks; Surrogate Modeling; Gaussian Process; Autoencoders; Uncertainty Quantification; Finite Element Method

Disciplines :

Engineering, computing & technology: Multidisciplinary, general & others

Author, co-author :

DESHPANDE, Saurabh ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)

RAPPEL, Hussein; University of Exeter [GB] > Faculty of Environment, Science and Economy

Hobbs, Mark; Rolls-Royce plc > Future Methods

BORDAS, Stéphane ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)

Lengiewicz, Jakub; PAN - Polish Academy of Sciences [PL] > Institute of Fundamental Technological Research

Language :

English

Title :

Gaussian process regression + deep neural network autoencoder for probabilistic surrogate modeling in nonlinear mechanics of solids

Publication date :

2024

Focus Area :

Computational Sciences

FnR Project :

FNR14782078 - Quantum-continuum Bridging, 2020 (01/09/2021-31/08/2024) - Stéphane Bordas

Name of the research project :

R-AGR-3325 - H2020-MSCA-ITN-2017-764644-RAINBOW - BORDAS Stéphane
R-AGR-3446 - H2020-MSCA-IF-MOrPhEM - part UL - LENGIEWICZ Jakub

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since 24 May 2024

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