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Solution of inverse problems with limited forward solver evaluations: a Bayesian perspective

Inverse problems, 2014-01, Vol.30 (1), p.15004-32

2014

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Physics-informed machine learning framework to model buildings from incomplete information

Journal of physics. Conference series, 2023-11, Vol.2600 (7), p.72013

2023

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Free energy computations by minimization of Kullback–Leibler divergence: An efficient adaptive biasing potential method for sparse representations

Journal of computational physics, 2012-05, Vol.231 (9), p.3849-3870

2012

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Crop physiology calibration in the CLM

Geoscientific Model Development, 2015-04, Vol.8 (4), p.1071-1083

2015

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Implementation of a hybrid healthcare model in rheumatic musculoskeletal diseases: 6-months results of the multicenter Digireuma study

BMC rheumatology, 2023-09, Vol.7 (1), p.32-32, Article 32

2023

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POS0563 USE OF A DIGITAL SOLUTION TO MONITOR CHRONIC INFLAMMATORY RHEUMATIC MUSCULOSKELETAL DISEASES: FINAL RESULTS OF THE DIGIREUMA STUDY

Annals of the rheumatic diseases, 2023-06, Vol.82 (Suppl 1), p.549-549

2023

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Non-invasive Detection of Bowel Sounds in Real-life Settings Using Spectrogram Zeros and Autoencoding

2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021, Vol.2021, p.915-919

2021

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An information field theory approach to Bayesian state and parameter estimation in dynamical systems

Journal of computational physics, 2024-09, Vol.512, p.113139, Article 113139

2024

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Learning to solve Bayesian inverse problems: An amortized variational inference approach using Gaussian and Flow guides

Journal of computational physics, 2024-08, Vol.511, p.113117, Article 113117

2024

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Physics-informed information field theory for modeling physical systems with uncertainty quantification

Journal of computational physics, 2023-08, Vol.486, p.112100, Article 112100

2023

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Multi-output local Gaussian process regression: Applications to uncertainty quantification

Journal of computational physics, 2012-07, Vol.231 (17), p.5718-5746

2012

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Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification

Journal of computational physics, 2018-12, Vol.375, p.565-588

2018

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Gaussian processes with built-in dimensionality reduction: Applications to high-dimensional uncertainty propagation

Journal of computational physics, 2016-09, Vol.321, p.191-223

2016

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Multi-output separable Gaussian process: Towards an efficient, fully Bayesian paradigm for uncertainty quantification

Journal of computational physics, 2013-05, Vol.241, p.212-239

2013

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Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks

Journal of computational physics, 2020-03, Vol.404, p.109120, Article 109120

2020

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Propagation of uncertainty in the mechanical and biological response of growing tissues using multi-fidelity Gaussian process regression

Computer methods in applied mechanics and engineering, 2020-02, Vol.359, p.112724, Article 112724

2020

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Physics-Informed Neural Networks for Solving Parametric Magnetostatic Problems

IEEE transactions on energy conversion, 2022-12, Vol.37 (4), p.2678-2689

2022

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Machine learning for high-dimensional dynamic stochastic economies

Journal of computational science, 2019-04, Vol.33, p.68-82

2019

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A stochastic optimization approach to coarse-graining using a relative-entropy framework

The Journal of chemical physics, 2013-01, Vol.138 (4), p.044313-044313

2013

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A Bayesian approach for probabilistic classification and inference of occupant thermal preferences in office buildings

Building and environment, 2017-06, Vol.118, p.323-343

2017

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Implementation of a self-tuned HVAC controller to satisfy occupant thermal preferences and optimize energy use

Energy and buildings, 2019-07, Vol.194, p.301-316

2019

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Inference of thermal preference profiles for personalized thermal environments with actual building occupants

Building and environment, 2019-01, Vol.148, p.714-729

2019

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Bayesian Optimal Design of Experiments for Inferring the Statistical Expectation of Expensive Black-Box Functions

Journal of mechanical design (1990), 2019-10, Vol.141 (10)

2019

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Bayesian inference of fiber orientation and polymer properties in short fiber-reinforced polymer composites

Composites science and technology, 2022-09, Vol.228 (C), p.109630, Article 109630

2022

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Uncertainty propagation using infinite mixture of Gaussian processes and variational Bayesian inference

Journal of computational physics, 2015-03, Vol.284 (C), p.291-333

2015

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Solution of inverse problems with limited forward solver evaluations: a Bayesian perspective

Physics-informed machine learning framework to model buildings from incomplete information

Free energy computations by minimization of Kullback–Leibler divergence: An efficient adaptive biasing potential method for sparse representations

Crop physiology calibration in the CLM

Implementation of a hybrid healthcare model in rheumatic musculoskeletal diseases: 6-months results of the multicenter Digireuma study

POS0563 USE OF A DIGITAL SOLUTION TO MONITOR CHRONIC INFLAMMATORY RHEUMATIC MUSCULOSKELETAL DISEASES: FINAL RESULTS OF THE DIGIREUMA STUDY

Non-invasive Detection of Bowel Sounds in Real-life Settings Using Spectrogram Zeros and Autoencoding

An information field theory approach to Bayesian state and parameter estimation in dynamical systems

Learning to solve Bayesian inverse problems: An amortized variational inference approach using Gaussian and Flow guides

Physics-informed information field theory for modeling physical systems with uncertainty quantification

Multi-output local Gaussian process regression: Applications to uncertainty quantification

Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification

Gaussian processes with built-in dimensionality reduction: Applications to high-dimensional uncertainty propagation

Multi-output separable Gaussian process: Towards an efficient, fully Bayesian paradigm for uncertainty quantification

Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks

Propagation of uncertainty in the mechanical and biological response of growing tissues using multi-fidelity Gaussian process regression

Physics-Informed Neural Networks for Solving Parametric Magnetostatic Problems

Machine learning for high-dimensional dynamic stochastic economies

A stochastic optimization approach to coarse-graining using a relative-entropy framework

A Bayesian approach for probabilistic classification and inference of occupant thermal preferences in office buildings

Implementation of a self-tuned HVAC controller to satisfy occupant thermal preferences and optimize energy use

Inference of thermal preference profiles for personalized thermal environments with actual building occupants

Bayesian Optimal Design of Experiments for Inferring the Statistical Expectation of Expensive Black-Box Functions

Bayesian inference of fiber orientation and polymer properties in short fiber-reinforced polymer composites

Uncertainty propagation using infinite mixture of Gaussian processes and variational Bayesian inference

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