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A data-driven computational homogenization method based on neural networks for the nonlinear anisotropic electrical response of graphene/polymer nanocomposites

Computational mechanics, 2019-08, Vol.64 (2), p.307-321

2019

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Stochastic Finite Element Methods: An Introduction

1st ed. 2018 edition., 2017

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Accelerated subset simulation with neural networks for reliability analysis

Computer methods in applied mechanics and engineering, 2012-06, Vol.223-224, p.70-80

2012

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Bayesian updating with subset simulation using artificial neural networks

Computer methods in applied mechanics and engineering, 2017-06, Vol.319, p.124-145

2017

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A spectral stochastic formulation for nonlinear framed structures

Probabilistic engineering mechanics, 2019-01, Vol.55, p.90-101

2019

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A new perspective on the solution of uncertainty quantification and reliability analysis of large-scale problems

Computer methods in applied mechanics and engineering, 2014-07, Vol.276, p.627-658

2014

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Grassmannian diffusion maps based surrogate modeling via geometric harmonics

International journal for numerical methods in engineering, 2022-08, Vol.123 (15), p.3507-3529

2022

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Data-driven uncertainty quantification in computational human head models

Computer methods in applied mechanics and engineering, 2022-08, Vol.398, p.115108, Article 115108

2022

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Epistemic uncertainty assessment using Incremental Dynamic Analysis and Neural Networks

Bulletin of earthquake engineering, 2016-02, Vol.14 (2), p.529-547

2016

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An adaptive spectral Galerkin stochastic finite element method using variability response functions

International journal for numerical methods in engineering, 2015-10, Vol.104 (3), p.185-208

2015

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Effect of Human Head Shape on the Risk of Traumatic Brain Injury: A Gaussian Process Regression-based Machine Learning Approach

Military medicine, 2024-08, Vol.189 (Supplement_3), p.608-617

2024

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Machine learning for the identification of phase transitions in interacting agent-based systems: A Desai-Zwanzig example

Physical review. E, 2024-07, Vol.110 (1), Article 014121

2024

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UQpy v4.1: Uncertainty quantification with Python

SoftwareX, 2023-12, Vol.24 (C), p.101561, Article 101561

2023

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Spectral representation-based neural network assisted stochastic structural mechanics

Engineering structures, 2015-02, Vol.84, p.382-394

2015

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Reliability Analysis

Stochastic Finite Element Methods, 2018, p.71-98

2018

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Stochastic Finite Element Method

Stochastic Finite Element Methods, 2018, p.47-70

2018

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Representation of a Stochastic Process

Stochastic Finite Element Methods, 2018, p.27-45

2018

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Stochastic Processes

Stochastic Finite Element Methods, 2018, p.1-26

2018

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UQpy: A general purpose Python package and development environment for uncertainty quantification

Journal of computational science, 2020-11, Vol.47 (C), p.101204, Article 101204

2020

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Subset simulation for problems with strongly non-Gaussian, highly anisotropic, and degenerate distributions

Computers & structures, 2021-03, Vol.245, p.106431, Article 106431

2021

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UQpy: A general purpose Python package and development environment for uncertainty quantification

Journal of computational science, 2020-09, Vol.47 (C)

2020

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Appendix A Probability Theory

Stochastic Finite Element Methods, 2017

2017

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Appendix B Random Variables

Stochastic Finite Element Methods, 2017

2017

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Appendix C Subset Simulation Aspects

Stochastic Finite Element Methods, 2017

2017

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Intro

Stochastic Finite Element Methods, 2017

2017

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A data-driven computational homogenization method based on neural networks for the nonlinear anisotropic electrical response of graphene/polymer nanocomposites

Stochastic Finite Element Methods: An Introduction

Accelerated subset simulation with neural networks for reliability analysis

Bayesian updating with subset simulation using artificial neural networks

A spectral stochastic formulation for nonlinear framed structures

A new perspective on the solution of uncertainty quantification and reliability analysis of large-scale problems

Grassmannian diffusion maps based surrogate modeling via geometric harmonics

Data-driven uncertainty quantification in computational human head models

Epistemic uncertainty assessment using Incremental Dynamic Analysis and Neural Networks

An adaptive spectral Galerkin stochastic finite element method using variability response functions

Effect of Human Head Shape on the Risk of Traumatic Brain Injury: A Gaussian Process Regression-based Machine Learning Approach

Machine learning for the identification of phase transitions in interacting agent-based systems: A Desai-Zwanzig example

UQpy v4.1: Uncertainty quantification with Python

Spectral representation-based neural network assisted stochastic structural mechanics

Reliability Analysis

Stochastic Finite Element Method

Representation of a Stochastic Process

Stochastic Processes

UQpy: A general purpose Python package and development environment for uncertainty quantification

Subset simulation for problems with strongly non-Gaussian, highly anisotropic, and degenerate distributions

UQpy: A general purpose Python package and development environment for uncertainty quantification

Appendix A Probability Theory

Appendix B Random Variables

Appendix C Subset Simulation Aspects

Intro

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