A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs | Paper Citation Record

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This paper cites A manifold learning approach to nonlinear model order reduction of quasi-static problems in solid mechanics.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs A manifold learning approach to nonlinear model order reduction of quasi-static problems in solid mechanics

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This paper cites A Sur vey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs A Sur vey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems

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This paper cites D esign across Length Scales: A Reduced- Order Model of Polycrystal Plasticity for the Control of Mic rostructure-Sensitive Material Properties.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs D esign across Length Scales: A Reduced- Order Model of Polycrystal Plasticity for the Control of Mic rostructure-Sensitive Material Properties

Reference 3

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This paper cites A L ocalized Reduced Basis Approach for Unﬁtted Domain Methods on Parameterized Geometries.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs A L ocalized Reduced Basis Approach for Unﬁtted Domain Methods on Parameterized Geometries

Reference 4

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This paper cites Fast Parametric Analysis of Trimmed Multi- Patch Isogeometric Kirchhoﬀ-Love Shells Using a Local Reduc ed Basis Method.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Fast Parametric Analysis of Trimmed Multi- Patch Isogeometric Kirchhoﬀ-Love Shells Using a Local Reduc ed Basis Method

Reference 5

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This paper cites Reduced Order Multiscale Mod eling of Nonlinear Processes in Heterogeneous Materials.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Reduced Order Multiscale Mod eling of Nonlinear Processes in Heterogeneous Materials

Reference 6

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This paper cites An Eﬀective Compu tational Tool for Parametric Studies and Identiﬁcation Problems in Materials Mechanics.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs An Eﬀective Compu tational Tool for Parametric Studies and Identiﬁcation Problems in Materials Mechanics

Reference 7

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This paper cites Learning Physics-Base d Models from Data: Perspectives from Inverse Problems and Model Reduction.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Learning Physics-Base d Models from Data: Perspectives from Inverse Problems and Model Reduction

Reference 8

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This paper cites Proper Orthogonal De composition and Radial Basis Functions in Material Characterization Based on Instrumented Indentat ion.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Proper Orthogonal De composition and Radial Basis Functions in Material Characterization Based on Instrumented Indentat ion

Reference 9

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This paper cites O n Calibration of Orthotropic Elastic-Plastic Constitutive Models for Paper Foils by Biaxial Tests and Inverse Analyses.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs O n Calibration of Orthotropic Elastic-Plastic Constitutive Models for Paper Foils by Biaxial Tests and Inverse Analyses

Reference 10

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This paper cites Reduced Basis Homog enization of Viscoelastic Composites.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Reduced Basis Homog enization of Viscoelastic Composites

Reference 11

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This paper cites Reduced-Order Multiscale Modeli ng of Plastic Deformations in 3D Alloys with Spatially Varying Porosity by Deﬂated Clustering Analysis.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Reduced-Order Multiscale Modeli ng of Plastic Deformations in 3D Alloys with Spatially Varying Porosity by Deﬂated Clustering Analysis

Reference 12

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This paper cites A Reduced Order Model for Geometrically Parame- terized Two-Scale Simulations of Elasto-Plastic Microstr uctures under Large Deformations.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs A Reduced Order Model for Geometrically Parame- terized Two-Scale Simulations of Elasto-Plastic Microstr uctures under Large Deformations

Reference 13

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This paper cites A Nonlinear Manifold-Based Reduced Order Model for Multiscale Analysis of Heterogeneous Hyperelastic Materi als.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs A Nonlinear Manifold-Based Reduced Order Model for Multiscale Analysis of Heterogeneous Hyperelastic Materi als

Reference 14

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This paper cites Design of 3D Statistically Simil ar Representative Volume Elements Based on Minkowski Functionals.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Design of 3D Statistically Simil ar Representative Volume Elements Based on Minkowski Functionals

Reference 15

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This paper cites Data-Driven Modellin g of the Multiaxial Yield Behaviour of Nanoporous Metals.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Data-Driven Modellin g of the Multiaxial Yield Behaviour of Nanoporous Metals

Reference 16

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This paper cites Nonuniform Transformation Field Analysis.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Nonuniform Transformation Field Analysis

Reference 17

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This paper cites Reduced Basis H ybrid Computational Homogenization Based on a Mixed Incremental Formulation.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Reduced Basis H ybrid Computational Homogenization Based on a Mixed Incremental Formulation

Reference 18

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This paper cites Comparison of Reduced Order Homogeni zation Techniques: pRBMOR, NUTF A and MxTF A.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Comparison of Reduced Order Homogeni zation Techniques: pRBMOR, NUTF A and MxTF A

Reference 19

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This paper cites Self-Consis tent Clustering Analysis: An Eﬃcient Multi- Scale Scheme for Inelastic Heterogeneous Materials.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Self-Consis tent Clustering Analysis: An Eﬃcient Multi- Scale Scheme for Inelastic Heterogeneous Materials

Reference 20

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This paper cites FE2 Multiscale in Linear Elasticity Based on Parametrized Microscale Models Using Proper Generalized Decomposition.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs FE2 Multiscale in Linear Elasticity Based on Parametrized Microscale Models Using Proper Generalized Decomposition

Reference 21

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This paper cites Model Order Reduction in Hype relasticity: A Proper Generalized Decom- position Approach.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Model Order Reduction in Hype relasticity: A Proper Generalized Decom- position Approach

Reference 22

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This paper cites High-Performance Model Reduct ion Techniques in Computational Multiscale Homogenization.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs High-Performance Model Reduct ion Techniques in Computational Multiscale Homogenization

Reference 23

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This paper cites A Mu ltilevel Projection-based Model Order Reduction Framework for Nonlinear Dynamic Multiscale Prob lems in Structural and Solid Mechanics.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs A Mu ltilevel Projection-based Model Order Reduction Framework for Nonlinear Dynamic Multiscale Prob lems in Structural and Solid Mechanics

Reference 24

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This paper cites Dimensi onal Hyper-Reduction of Nonlinear Finite El- ement Models via Empirical Cubature.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Dimensi onal Hyper-Reduction of Nonlinear Finite El- ement Models via Empirical Cubature

Reference 25

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This paper cites A Numerical Study of Diﬀerent Pro jection-Based Model Reduction Techniques Applied to Computational Homogenisation.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs A Numerical Study of Diﬀerent Pro jection-Based Model Reduction Techniques Applied to Computational Homogenisation

Reference 26

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This paper cites Mi cromechanics-Based Surrogate Models for the Response of Composites: A Critical Comparison betwe en a Classical Mesoscale Constitutive Model, Hyper-Reduction and Neural Networks.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Mi cromechanics-Based Surrogate Models for the Response of Composites: A Critical Comparison betwe en a Classical Mesoscale Constitutive Model, Hyper-Reduction and Neural Networks

Reference 27

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This paper cites High Performance Reduction Tech nique for Multiscale Finite Element Modeling (HPR-FE2): Towards Industrial Multiscale FE Software.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs High Performance Reduction Tech nique for Multiscale Finite Element Modeling (HPR-FE2): Towards Industrial Multiscale FE Software

Reference 28

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This paper cites A Monol ithic Hyper ROM FE2 Method with Clustered Training at Finite Deformations.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs A Monol ithic Hyper ROM FE2 Method with Clustered Training at Finite Deformations

Reference 29

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs E3C for Computational Homogenization in Nonlinear Mechanics

Reference 30

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This paper cites A Nonlinear Data-Driven Reduced Order Model for Com- putational Homogenization with Physics/Pattern-Guided Sampling.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs A Nonlinear Data-Driven Reduced Order Model for Com- putational Homogenization with Physics/Pattern-Guided Sampling

Reference 31

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This paper cites Data-Driven Multiscale Finite-Element Method Using Deep Neural Network Combined with Proper Orthogonal Decomposition.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Data-Driven Multiscale Finite-Element Method Using Deep Neural Network Combined with Proper Orthogonal Decomposition

Reference 32

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Opera tor Inference for Non-Intrusive Model Re- duction with Quadratic Manifolds

Reference 33

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This paper cites Uncertainty Quantiﬁcation for N onlinear Solid Mechanics Using Reduced Order Models with Gaussian Process Regression.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Uncertainty Quantiﬁcation for N onlinear Solid Mechanics Using Reduced Order Models with Gaussian Process Regression

Reference 34

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This paper cites Deep-HyROMnet: A Deep Learning-Based Op- erator Approximation for Hyper-Reduction of Nonlinear Par ametrized PDEs.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Deep-HyROMnet: A Deep Learning-Based Op- erator Approximation for Hyper-Reduction of Nonlinear Par ametrized PDEs

Reference 35

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Non-Intrusive Parametric Hyper- Reduction for Nonlinear Structural Finite Ele- ment Formulations

Reference 36

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Hyper-reduction of Mechanical Models Involving Internal Variables

Reference 37

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Karhunen–Lo` eve Procedur e for Gappy Data

Reference 38

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Unsteady Flow Sensing and Estimation via th e Gappy Proper Orthogonal Decomposition

Reference 39

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This paper cites A ‘Best Point s’ Interpolation Method for Eﬃcient Approx- imation of Parametrized Functions.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs A ‘Best Point s’ Interpolation Method for Eﬃcient Approx- imation of Parametrized Functions

Reference 40

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Missing Point Estimation in Mod els Described by Proper Orthogonal De- composition

Reference 41

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Nonlinea r Model Reduction via Discrete Empirical Interpolation

Reference 42

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This paper cites DRAFT: A MODI FIED DISCRETE EMPIRICAL IN- TERPOLATION METHOD FOR REDUCING NON-LINEAR STRUCTURAL FIN ITE ELEMENT MODELS.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs DRAFT: A MODI FIED DISCRETE EMPIRICAL IN- TERPOLATION METHOD FOR REDUCING NON-LINEAR STRUCTURAL FIN ITE ELEMENT MODELS

Reference 43

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs S-OPT: A Points Selection Algo rithm for Hyper-Reduction in Reduced Order Models

Reference 44

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This paper cites Eﬃcient Non-linear Model Reduction via a Least-squares Petrov–Galerkin Projection and Compressiv e Tensor Approximations.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Eﬃcient Non-linear Model Reduction via a Least-squares Petrov–Galerkin Projection and Compressiv e Tensor Approximations

Reference 45

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This paper cites The GNAT method for nonlinear model reduction: effective implementation and application to computational fluid dynamics and turbulent flows.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs The GNAT method for nonlinear model reduction: effective implementation and application to computational fluid dynamics and turbulent flows

Reference 46

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Gal erkin v. Least-Squares Petrov–Galerkin Pro- jection in Nonlinear Model Reduction

Reference 47

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This paper cites L1-Based Reduced Over Collocation a nd Hyper Reduction for Steady State and Time-Dependent Nonlinear Equations.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs L1-Based Reduced Over Collocation a nd Hyper Reduction for Steady State and Time-Dependent Nonlinear Equations

Reference 48

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This paper cites Dimensional Reduction of Nonlin ear Finite Element Dynamic Models with Finite Rotations and Energy-based Mesh Sampling and Weight ing for Computational Eﬃciency.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Dimensional Reduction of Nonlin ear Finite Element Dynamic Models with Finite Rotations and Energy-based Mesh Sampling and Weight ing for Computational Eﬃciency

Reference 49

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Statistically Compatible Hyper-R eduction for Computational Homogeniza- tion

Reference 50

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs The Reduced Model Multiscale M ethod (R3M) for the Non-Linear Ho- mogenization of Hyperelastic Media at Finite Strains

Reference 51

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Computational Ho mogenization for Nonlinear Conduction in Heterogeneous Materials Using Model Reduction

Reference 52

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This paper cites Displacement-Based Multis cale Modeling of Fiber-Reinforced Composites by Means of Proper Orthogonal Decomposition.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Displacement-Based Multis cale Modeling of Fiber-Reinforced Composites by Means of Proper Orthogonal Decomposition

Reference 53

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs A Tutorial on the Proper Orthogonal Deco mposition

Reference 54

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs N onlinear Model Order Reduction Based on Local Reduced-order Bases

Reference 55

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Fast Local Reduced Basis Updates for the Eﬃcient Reduction of Nonlinear Systems with Hyper-Redu ction

Reference 56

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This paper cites Model Order Reduction Assisted by Deep Neural Networks (ROM-net).

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Model Order Reduction Assisted by Deep Neural Networks (ROM-net)

Reference 57

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Physics-Informed Cluster Analys is and a Priori Eﬃciency Criterion for the Construction of Local Reduced-Order Bases

Reference 58

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Uncertainty Quantiﬁcation for In dustrial Numerical Simulation Using Dictionar- ies of Reduced Order Models

Reference 59

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs A Quadratic Manifold for Model Orde r Reduction of Nonlinear Structural Dynam- ics

Reference 60

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Hyper-reduction over nonlinear manifolds for large nonlinear mechanical systems

Reference 61

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Quadratic Approxi mation Manifold for Mitigating the Kol- mogorov Barrier in Nonlinear Projection-Based Model Order Reduction

Reference 62

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs A Fast and Accurate Physics-Inform ed Neural Network Reduced Order Model with Shallow Masked Autoencoder

Reference 63

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs A Com prehensive Deep Learning-Based Approach to Reduced Order Modeling of Nonlinear Time-Dependent Para metrized PDEs

Reference 64

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This paper cites POD-DL-ROM: Enha ncing Deep Learning-Based Reduced Order Models for Nonlinear Parametrized PDEs by Proper Orth ogonal Decomposition.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs POD-DL-ROM: Enha ncing Deep Learning-Based Reduced Order Models for Nonlinear Parametrized PDEs by Proper Orth ogonal Decomposition

Reference 65

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Neura l-Network-Augmented Projection-Based Model Order Reduction for Mitigating the Kolmogorov Barrie r to Reducibility

Reference 66

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Nonlinear Dimensio nality Reduction by Locally Linear Embed- ding

Reference 67

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This paper cites Linear and Nonlinear Dimensionality Reduction from Fluid Mechanics to Machine Learning.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Linear and Nonlinear Dimensionality Reduction from Fluid Mechanics to Machine Learning

Reference 68

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Nonlinear Manifold Learning for Model Reduction in Finite Elas- todynamics

Reference 69

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Nonlinear Dimensionality Reductio n for Parametric Problems: A Kernel Proper Orthogonal Decomposition

Reference 70

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Nonlinear Model Reduction via an Adaptive Weighting of Snapshots

Reference 71

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs VpROM: A novel Variational AutoEncoder-boosted Reduced Order Model for the treatment of parametric dependencies in nonlinear systems

Reference 72

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Simulation-Free Hyper-R eduction for Geometrically Nonlinear Struc- tural Dynamics: A Quadratic Manifold Lifting Approach

Reference 73

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Empirical sparse regression on quadratic manifolds

Reference 74

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Hyper-Reduced Autoencoders for Efficient and Accurate Nonlinear Model Reductions

Reference 75

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This paper cites Explicable Hyper-Reduced Order Models on Nonlinearly Approximated Solution Manifolds of Compres sible and Incompressible Navier-Stokes Equations.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Explicable Hyper-Reduced Order Models on Nonlinearly Approximated Solution Manifolds of Compres sible and Incompressible Navier-Stokes Equations

Reference 76

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Model Reduction of D ynamical Systems on Nonlinear Manifolds Using Deep Convolutional Autoencoders

Reference 77

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Non-linear manifold ROM with Convolutional Autoencoders and Reduced Over-Collocation method

Reference 78

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Explicable hyper-reduced order models on nonlinearly approximated solution manifolds of compressible and incompressible Navier-Stokes equations

Reference 79

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Numerical Approximation of Parametrized Problems in Cardiac Electrophysiology by a Local Reduced Ba sis Method

Reference 80

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This paper cites A Local Basis Approximati on Approach for Nonlinear Parametric Model Order Reduction.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs A Local Basis Approximati on Approach for Nonlinear Parametric Model Order Reduction

Reference 81

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs POD–DEIM Model Or der Reduction for Strain-Softening Vis- coplasticity

Reference 82

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs In Situ Ada ptive Reduction of Nonlinear Multiscale Structural Dynamics Models

Reference 83

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs A Nonlinear Manifold Model Order R eduction Approach for Thermo-mechanically Coupled Plasticity

Reference 84

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Reduced-Order Modelling and Ho mogenisation in Magneto-Mechanics: A Nu- merical Comparison of Established Hyper-Reduction Method s

Reference 85

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Preserv ing Lagrangian Structure in Nonlinear Model Reduction with Application to Structural Dynamics

Reference 86

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Struct ure-Preserving Model Reduction for Nonlinear Port-Hamiltonian Systems

Reference 87

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Gradient- Preserving Hyper-Reduction of Nonlinear Dy- namical Systems via Discrete Empirical Interpolation

Reference 88

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Bridging Analytical and Computational Ho- mogenisation for Nonlinear Multiscale Problems: A Reduced Order Modelling Approach for a Damage Problem

Reference 89

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Fast Multiscale Reservoi r Simulations Using POD-DEIM Model Re- duction

Reference 90

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs M odel Reduction for Multiscale Lithium-Ion Battery Simulation

Reference 91

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Observation e5d8ee8a-6c5a-4883-95c0-5fad8cb26631 · outbound

This paper cites Integration Eﬃciency for Model Reduction in Micro-Mechanical Analyses.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Integration Eﬃciency for Model Reduction in Micro-Mechanical Analyses

Reference 92

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs A Two-Step Sequential Hyper-R eduction Method for Eﬃcient Concur- rent Nonlinear FE2 Analyses

Reference 93

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs High Performance Reduced Order M odeling Techniques Based on Optimal Energy Quadrature: Application to Geometrically Non-line ar Multiscale Inelastic Material Modeling

Reference 94

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Empirical Hyper Element Integration Method (EHEIM) with Unified Integration Criteria for Efficient Hyper Reduced FE$^2$ Simulations

Reference 95

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A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Reduced-Order Modeling for Second-Order Computational Homogenization With Applications to Geometrically Parameterized Elastomeric Metamaterials

Reference 96

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This paper cites Finite Strain Homogeni zation Using a Reduced Basis and Eﬃcient Sampling.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Finite Strain Homogeni zation Using a Reduced Basis and Eﬃcient Sampling

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This paper cites POD-based Mod el Reduction with Empirical Interpolation Applied to Nonlinear Elasticity.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs POD-based Mod el Reduction with Empirical Interpolation Applied to Nonlinear Elasticity

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This paper cites An Algorithmic Comparison of the H yper-Reduction and the Discrete Empiri- cal Interpolation Method for a Nonlinear Thermal Problem.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs An Algorithmic Comparison of the H yper-Reduction and the Discrete Empiri- cal Interpolation Method for a Nonlinear Thermal Problem

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This paper cites A Hyper-Reduction Computation al Method for Accelerated Modeling of Ther- mal Cycling-Induced Plastic Deformations.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs A Hyper-Reduction Computation al Method for Accelerated Modeling of Ther- mal Cycling-Induced Plastic Deformations

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