Data-Driven Model Order Reduction for Diffeomorphic Image Registration

Lecture Notes in Computer Science - Information Processing in Medical Imaging ◽

10.1007/978-3-030-20351-1_54 ◽

2019 ◽

pp. 694-705 ◽

Cited By ~ 1

Author(s):

Jian Wang ◽

Wei Xing ◽

Robert M. Kirby ◽

Miaomiao Zhang

Keyword(s):

Image Registration ◽

Model Order Reduction ◽

Order Reduction ◽

Data Driven ◽

Model Order ◽

Diffeomorphic Image Registration

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A hybrid data-driven model order reduction strategy for flexible multibody systems considering impact and friction

Mechanism and Machine Theory ◽

10.1016/j.mechmachtheory.2021.104649 ◽

2022 ◽

Vol 169 ◽

pp. 104649

Author(s):

Ningning Song ◽

Haijun Peng ◽

Ziyun Kan

Keyword(s):

Multibody Systems ◽

Model Order Reduction ◽

Order Reduction ◽

Flexible Multibody Systems ◽

Data Driven ◽

Model Order ◽

Reduction Strategy ◽

Flexible Multibody ◽

Hybrid Data

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Nonlinear data-driven model order reduction applied to circuit-field magnetic problem

IEEE Transactions on Magnetics ◽

10.1109/tmag.2021.3108454 ◽

2021 ◽

pp. 1-1

Author(s):

Antoine Pierquin ◽

Thomas Henneron

Keyword(s):

Model Order Reduction ◽

Order Reduction ◽

Data Driven ◽

Model Order

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Data-driven model order reduction via Loewner approach for fast frequency sweep in hybrid BI-FEM solution in large finite frequency selective surfaces

2017 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization for RF, Microwave, and Terahertz Applications (NEMO) ◽

10.1109/nemo.2017.7964262 ◽

2017 ◽

Author(s):

Valentin de la Rubia ◽

Zhen Peng

Keyword(s):

Model Order Reduction ◽

Order Reduction ◽

Data Driven ◽

Frequency Selective Surfaces ◽

Finite Frequency ◽

Model Order ◽

Frequency Sweep ◽

Frequency Selective

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Data-Driven Parameterized Model Order Reduction Using z-Domain Multivariate Orthonormal Vector Fitting Technique

Lecture Notes in Electrical Engineering - Model Reduction for Circuit Simulation ◽

10.1007/978-94-007-0089-5_7 ◽

2011 ◽

pp. 141-148 ◽

Cited By ~ 1

Author(s):

Francesco Ferranti ◽

Dirk Deschrijver ◽

Luc Knockaert ◽

Tom Dhaene

Keyword(s):

Model Order Reduction ◽

Order Reduction ◽

Data Driven ◽

Model Order ◽

Vector Fitting ◽

Z Domain ◽

Parameterized Model ◽

Parameterized Model Order Reduction

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Data-Driven Model Order Reduction of Linear Switched Systems in the Loewner Framework

SIAM Journal on Scientific Computing ◽

10.1137/17m1120233 ◽

2018 ◽

Vol 40 (2) ◽

pp. B572-B610 ◽

Cited By ~ 5

Author(s):

I. V. Gosea ◽

M. Petreczky ◽

A. C. Antoulas

Keyword(s):

Switched Systems ◽

Model Order Reduction ◽

Order Reduction ◽

Data Driven ◽

Model Order

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Development of an Efficient Nonlinear Structural Analysis Using Data-driven Model Order Reduction

Transactions of the Korean Society for Noise and Vibration Engineering ◽

10.5050/ksnve.2021.31.6.604 ◽

2021 ◽

Vol 31 (6) ◽

pp. 604-613

Author(s):

Hyejin Kim ◽

Haeseong Cho ◽

Sihun Lee ◽

SangJoon Shin ◽

Haedeong Kim

Keyword(s):

Structural Analysis ◽

Model Order Reduction ◽

Order Reduction ◽

Data Driven ◽

Model Order ◽

Nonlinear Structural Analysis ◽

Nonlinear Structural ◽

Using Data

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Smart parts: Data-driven model order reduction for nonlinear mechanical assemblies

Finite Elements in Analysis and Design ◽

10.1016/j.finel.2021.103682 ◽

2022 ◽

Vol 200 ◽

pp. 103682

Author(s):

Aarohi Shah ◽

Julian J. Rimoli

Keyword(s):

Model Order Reduction ◽

Order Reduction ◽

Data Driven ◽

Model Order ◽

Mechanical Assemblies ◽

Nonlinear Mechanical

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A data‐driven model order reduction approach for Stokes flow through random porous media

PAMM ◽

10.1002/pamm.201800314 ◽

2018 ◽

Vol 18 (1) ◽

Author(s):

Constantin Grigo ◽

Phaedon‐Stelios Koutsourelakis

Keyword(s):

Porous Media ◽

Stokes Flow ◽

Model Order Reduction ◽

Order Reduction ◽

Data Driven ◽

Model Order ◽

Flow Through ◽

Random Porous Media ◽

Reduction Approach

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Some applications of compressed sensing in computational mechanics: model order reduction, manifold learning, data-driven applications and nonlinear dimensionality reduction

Computational Mechanics ◽

10.1007/s00466-019-01703-5 ◽

2019 ◽

Vol 64 (5) ◽

pp. 1259-1271

Author(s):

R. Ibañez ◽

E. Abisset-Chavanne ◽

E. Cueto ◽

A. Ammar ◽

J. -L. Duval ◽

...

Keyword(s):

Compressed Sensing ◽

Dimensionality Reduction ◽

Manifold Learning ◽

Model Order Reduction ◽

Order Reduction ◽

Data Driven ◽

Nonlinear Dimensionality Reduction ◽

Model Order ◽

Mechanics Model ◽

Learning Data

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Data-driven model order reduction for granular media

Computational Particle Mechanics ◽

10.1007/s40571-020-00387-6 ◽

2021 ◽

Author(s):

Erik Wallin ◽

Martin Servin

Keyword(s):

Model Order Reduction ◽

Granular Media ◽

Order Reduction ◽

Contact Forces ◽

Data Driven ◽

System Control ◽

Mean Flow ◽

Model Order ◽

Gaseous State ◽

Data Driven Approach

AbstractWe investigate the use of reduced-order modelling to run discrete element simulations at higher speeds. Taking a data-driven approach, we run many offline simulations in advance and train a model to predict the velocity field from the mass distribution and system control signals. Rapid model inference of particle velocities replaces the intense process of computing contact forces and velocity updates. In coupled DEM and multibody system simulation, the predictor model can be trained to output the interfacial reaction forces as well. An adaptive model order reduction technique is investigated, decomposing the media in domains of solid, liquid, and gaseous state. The model reduction is applied to solid and liquid domains where the particle motion is strongly correlated with the mean flow, while resolved DEM is used for gaseous domains. Using a ridge regression predictor, the performance is tested on simulations of a pile discharge and bulldozing. The measured accuracy is about 90% and 65%, respectively, and the speed-up range between 10 and 60.

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