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Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders

Journal of computational physics, 2020-03, Vol.404, p.108973, Article 108973

2020

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Conservative model reduction for finite-volume models

Journal of computational physics, 2018-10, Vol.371 (C), p.280-314

2018

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Machine-learning error models for approximate solutions to parameterized systems of nonlinear equations

Computer methods in applied mechanics and engineering, 2019-05, Vol.348 (C), p.250-296

2019

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Error modeling for surrogates of dynamical systems using machine learning

International journal for numerical methods in engineering, 2017-12, Vol.112 (12), p.1801-1827

2017

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Time-series machine-learning error models for approximate solutions to parameterized dynamical systems

Computer methods in applied mechanics and engineering, 2020-06, Vol.365 (C), p.112990, Article 112990

2020

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Online adaptive basis refinement and compression for reduced-order models via vector-space sieving

Computer methods in applied mechanics and engineering, 2020-06, Vol.364 (C), p.112931, Article 112931

2020

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Windowed least-squares model reduction for dynamical systems

Journal of computational physics, 2021-02, Vol.426, p.109939, Article 109939

2021

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Preserving general physical properties in model reduction of dynamical systems via constrained‐optimization projection

International journal for numerical methods in engineering, 2021-07, Vol.122 (14), p.3368-3399

2021

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Adaptive h-refinement for reduced-order models

International journal for numerical methods in engineering, 2015-05, Vol.102 (5), p.1192-1210

Carlberg, Kevin

2015

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A method for interpolating on manifolds structural dynamics reduced-order models

International journal for numerical methods in engineering, 2009-11, Vol.80 (9), p.1241-1258

2009

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Projection-tree reduced-order modeling for fast N-body computations

Journal of computational physics, 2022-06, Vol.459, p.111141, Article 111141

2022

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Space--Time Least-Squares Petrov--Galerkin Projection for Nonlinear Model Reduction

SIAM journal on scientific computing, 2019-01, Vol.41 (1), p.A26-A58

2019

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Galerkin v. least-squares Petrov–Galerkin projection in nonlinear model reduction

Journal of computational physics, 2017-02, Vol.330, p.693-734

2017

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Domain-decomposition least-squares Petrov–Galerkin (DD-LSPG) nonlinear model reduction

Computer methods in applied mechanics and engineering, 2021-10, Vol.384, p.113997, Article 113997

2021

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A low-cost, goal-oriented 'compact proper orthogonal decomposition' basis for model reduction of static systems

International journal for numerical methods in engineering, 2011-04, Vol.86 (3), p.381-402

2011

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The GNAT method for nonlinear model reduction: Effective implementation and application to computational fluid dynamics and turbulent flows

Journal of computational physics, 2013-06, Vol.242, p.623-647

2013

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Preserving Lagrangian Structure in Nonlinear Model Reduction with Application to Structural Dynamics

SIAM journal on scientific computing, 2015-01, Vol.37 (2), p.B153-B184

2015

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Efficient non-linear model reduction via a least-squares Petrov-Galerkin projection and compressive tensor approximations

International journal for numerical methods in engineering, 2011-04, Vol.86 (2), p.155-181

2011

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Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders

Journal of computational physics, 2019-11, Vol.404

2019

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Model reduction for the material point method via an implicit neural representation of the deformation map

Journal of computational physics, 2023-04, Vol.478, p.111908, Article 111908

2023

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The ROMES Method for Statistical Modeling of Reduced-Order-Model Error

SIAM/ASA journal on uncertainty quantification, 2015-01, Vol.3 (1), p.116-145

2015

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Domain-decomposition least-squares Petrov–Galerkin (DD-LSPG) nonlinear model reduction

Computer methods in applied mechanics and engineering, 2021-10, Vol.384 (C)

2021

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Domain-decomposition least-squares Petrov–Galerkin (DD-LSPG) nonlinear model reduction

Computer methods in applied mechanics and engineering, 2021-06, Vol.384 (N/A)

2021

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Preconditioned least‐squares Petrov–Galerkin reduced order models

International journal for numerical methods in engineering, 2022-10, Vol.123 (20), p.4809-4843

2022

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Windowed least-squares model reduction for dynamical systems

Journal of computational physics, 2020-10, Vol.426

2020

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Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders

Conservative model reduction for finite-volume models

Machine-learning error models for approximate solutions to parameterized systems of nonlinear equations

Error modeling for surrogates of dynamical systems using machine learning

Time-series machine-learning error models for approximate solutions to parameterized dynamical systems

Online adaptive basis refinement and compression for reduced-order models via vector-space sieving

Windowed least-squares model reduction for dynamical systems

Preserving general physical properties in model reduction of dynamical systems via constrained‐optimization projection

Adaptive h-refinement for reduced-order models

A method for interpolating on manifolds structural dynamics reduced-order models

Projection-tree reduced-order modeling for fast N-body computations

Space--Time Least-Squares Petrov--Galerkin Projection for Nonlinear Model Reduction

Galerkin v. least-squares Petrov–Galerkin projection in nonlinear model reduction

Domain-decomposition least-squares Petrov–Galerkin (DD-LSPG) nonlinear model reduction

A low-cost, goal-oriented 'compact proper orthogonal decomposition' basis for model reduction of static systems

The GNAT method for nonlinear model reduction: Effective implementation and application to computational fluid dynamics and turbulent flows

Preserving Lagrangian Structure in Nonlinear Model Reduction with Application to Structural Dynamics

Efficient non-linear model reduction via a least-squares Petrov-Galerkin projection and compressive tensor approximations

Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders

Model reduction for the material point method via an implicit neural representation of the deformation map

The ROMES Method for Statistical Modeling of Reduced-Order-Model Error

Domain-decomposition least-squares Petrov–Galerkin (DD-LSPG) nonlinear model reduction

Domain-decomposition least-squares Petrov–Galerkin (DD-LSPG) nonlinear model reduction

Preconditioned least‐squares Petrov–Galerkin reduced order models

Windowed least-squares model reduction for dynamical systems

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