Search Results - Reduced Convolutional Autoencoder~

Data-driven non-intrusive reduced order modelling of selective laser melting additive manufacturing process using proper orthogonal decomposition and convolutional autoencoder

Authors: Shubham Chaudhry Azzedine Abdedou Azzeddine Soulaïmani

Source: Advanced Modeling and Simulation in Engineering Sciences, Vol 12, Iss 1, Pp 1-23 (2025)

Subject Terms: Reduced-order model, Convolutional autoencoder, Additive manufacturing, Deep learning, Proper orthogonal decomposition, Mechanics of engineering. Applied mechanics, TA349-359, Systems engineering, TA168

File Description: electronic resource

Relation: https://doaj.org/toc/2213-7467

Access URL: https://doaj.org/article/1acb9d02b46540cbb5ab687871ded7c0

A turbulence reduced order model based on non-interpolated convolutional autoencoder

Authors: WU Pin ZHANG Bo SONG Chao et al.

Source: Xibei Gongye Daxue Xuebao, Vol 43, Iss 1, Pp 149-153 (2025)

Subject Terms: reduced-order model, non-interpolated convolutional autoencoder, reduced-dimensional reconstruction, Motor vehicles. Aeronautics. Astronautics, TL1-4050

File Description: electronic resource

Relation: https://www.jnwpu.org/articles/jnwpu/full_html/2025/01/jnwpu2025431p149/jnwpu2025431p149.html; https://doaj.org/toc/1000-2758; https://doaj.org/toc/2609-7125

Access URL: https://doaj.org/article/a60236e02ab0428a9bb44d6bbe63c9d5

Slim multi-scale convolutional autoencoder-based reduced-order models for interpretable features of a complex dynamical system

Authors: Teutsch, Philipp Pfeffer, Philipp Ghazijahani, Mohammad Sharifi et al.

Index Terms: Physics - Fluid Dynamics, Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/2501.03070

Coupling of a lightweight model of reduced convolutional autoencoder with linear SVM classifier to detect brain tumours on FPGA

Authors: Soumita Chatterjee Soumya Pandit Arpita Das

Source: Expert Systems with Applications. 290:128444

Reduced-order modeling for stochastic large-scale and time-dependent flow problems using deep spatial and temporal convolutional autoencoders

Authors: Azzedine Abdedou Azzeddine Soulaimani

Source: Advanced Modeling and Simulation in Engineering Sciences, Vol 10, Iss 1, Pp 1-27 (2023)

Subject Terms: Uncertainty propagation, Reduced-order modeling, Deep learning, Convolutional autoencoders, Mechanics of engineering. Applied mechanics, TA349-359, Systems engineering, TA168

File Description: electronic resource

Relation: https://doaj.org/toc/2213-7467

Access URL: https://doaj.org/article/7b3e7a3a2ef34d2db2cba87bb3d145f3

Physics-assisted multi-scale convolutional autoencoder for turbulence reduced-order modeling

Authors: Mengxue Lu Zi Ye Longye Qiao et al.

Source: Physics of Fluids. 37

Slim multi-scale convolutional autoencoder-based reduced-order models for interpretable features of a complex dynamical system

Authors: Philipp Teutsch Philipp Pfeffer Mohammad Sharifi Ghazijahani et al.

Source: APL Machine Learning, Vol 3, Iss 1, Pp 016112-016112-15 (2025)

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Physics, QC1-999, Electronic computers. Computer science, Fluid Dynamics (physics.flu-dyn), FOS: Physical sciences, QA75.5-76.95, Physics - Fluid Dynamics, Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/2501.03070
https://doaj.org/article/236f8e1f2be64001a1d9c1d7b38ae1f2

Efficient Coupling of Urban Wind Fields and Drone Flight Dynamics Using Convolutional Autoencoders

Authors: Zack Krawczyk Ryan Paul Kursat Kara

Source: Drones, Vol 9, Iss 11, p 802 (2025)

Subject Terms: urban wind fields, urban boundary layer, convolutional autoencoders, Reduced-Order Modeling (ROM), Non-Intrusive ROM, Large-Eddy Simulation (LES), Motor vehicles. Aeronautics. Astronautics, TL1-4050

File Description: electronic resource

Relation: https://www.mdpi.com/2504-446X/9/11/802; https://doaj.org/toc/2504-446X

Access URL: https://doaj.org/article/1d4b41821c8e43b9bcc79eda0400cdc8

Reduced-order modeling via convolutional autoencoder for emulating combustion of hydrogen/methane fuel blends

Authors: Siyu Ding Chenxu Ni Xu Chu et al.

Source: Combustion and Flame. 274:113981

Reduced-Order Model of Coal Seam Gas Extraction Pressure Distribution Based on Deep Neural Networks and Convolutional Autoencoders.

Authors: Hao, Tianxuan Zhao, Lizhen Du, Yang et al.

Source: Information. Nov2024, Vol. 15 Issue 11, p733. 20p.

Subject Terms: *Artificial neural networks, Gas well drilling, Coalbed methane, Gas extraction, Reduced-order models

PREDICTION OF AIRFOIL TABLE UNDER SUBSONIC FLOW CONDITIONS VIA CONVOLUTIONAL AUTOENCODER-BASED REDUCED-ORDER MODELING

Authors: Y.E. Kang D. Lee K. Yee

Source: Journal of Computational Fluids Engineering. 27:9-19

Nonlinear reduced-order modeling with a Graph Convolutional Autoencoder for time-domain electromagnetics

Authors: Filippin, Carlotta Strazzullo, Maria Lanteri, Stéphane et al.

Contributors: Filippin, Carlotta Strazzullo, Maria Lanteri, Stéphane et al.

Subject Terms: nanophotonique, nanophotonics électromagnétisme computationnel, réseaux de neurones, nonlinear phenomena, graph neural networks, computational electromagnetics, [MATH.MATH-NA] Mathematics [math]/Numerical Analysis [math.NA], phénomènes non linéaires, modélisation d'ordre réduit, reduced order modeling

File Description: application/pdf

Access URL: https://hal.science/hal-04867474v1

Reduced Biquaternion Stacked Denoising Convolutional AutoEncoder for RGB-D Image Classification

Authors: Xiang Huang Shan Gai

Source: IEEE Signal Processing Letters. 28:1205-1209

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Access URL: https://ieeexplore.ieee.org/document/9449952/
http://ui.adsabs.harvard.edu/abs/2021ISPL...28.1205H/abstract
https://dblp.uni-trier.de/db/journals/spl/spl28.html#HuangG21

Reduced-order modeling for stochastic large-scale and time-dependent flow problems using deep spatial and temporal convolutional autoencoders

Authors: Abdedou, Azzedine Soulaimani, Azzedine

Index Terms: Article publié dans une revue, révisé par les pairs, Non évalué par les pairs

URL: https://espace2.etsmtl.ca/id/eprint/26755/
https://doi.org/10.1186/s40323-023-00244-0
https://espace2.etsmtl.ca/id/eprint/26755
https://doi.org/10.1186/s40323-023-00244-0
10.1186/s40323-023-00244-0

APPLICATION OF REDUCED ORDER MODEL FOR METHANE JET FLAME BASED ON DEEP CONVOLUTIONAL AUTOENCODER

Authors: K.Y. Huh W.J. Lee

Source: Journal of Computational Fluids Engineering. 26:44-51

Subject Terms: 0209 industrial biotechnology, 0203 mechanical engineering, 02 engineering and technology

Access URL: https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART002701188

Data-driven non-intrusive reduced order modelling of selective laser melting additive manufacturing process using proper orthogonal decomposition and convolutional autoencoder

Authors: Shubham Chaudhry Azzedine Abdedou Azzeddine Soulaimani

Source: Advanced Modeling and Simulation in Engineering Sciences. 12

High performance computing and artificial intelligence algorithms for dimensionality reduction of turbulent flows

Authors: Eiximeno Franch, Benet

Contributors: University/Department: Universitat Politècnica de Catalunya. Departament de Física

Thesis Advisors: Rodríguez Pérez, Ivette María, Lehmkuhl Barba, Oriol

Source: TDX (Tesis Doctorals en Xarxa)

Subject Terms: reduced order models, high performance computing, deep learning, singular value decomposition, variational autoencoders, Àrees temàtiques de la UPC::Física, Àrees temàtiques de la UPC::Informàtica, 531/534 - Mecànica. Vibracions. Acústica, 004 - Informàtica

File Description: application/pdf

Access URL: http://hdl.handle.net/10803/695826
https://dx.doi.org/10.5821/dissertation-2117-446576

Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: Comparison with linear subspace techniques

Authors: Kadeethum, T. Ballarin, Francesco Choi, Y. et al.

Index Terms: Data-driven, Deep convolutional autoencoder, Finite element, Non-intrusive, Nonlinear problem, Reduced order modeling, Settore MAT/08 - ANALISI NUMERICA, info:eu-repo/semantics/article

URL: https://hdl.handle.net/10807/202829
info:eu-repo/semantics/altIdentifier/wos/WOS:000803685400003
volume:160
issue:NA
firstpage:104098
lastpage:N/A
issueyear:2022
journal:ADVANCES IN WATER RESOURCES

Convolutional autoencoder based reduced order modelling for physics problems

Authors: van Malsen, Hans (author)

Index Terms: master thesis

URL: http://resolver.tudelft.nl/uuid:f347263c-036c-467a-8b82-b8a257aed463

Convolutional Autoencoder for the Spatiotemporal Latent Representation of Turbulence

Authors: Doan, Nguyen Anh Khoa Racca, Alberto Magri, Luca et al.

Contributors: Doan, Nguyen Anh Khoa Racca, Alberto Magri, Luca et al.

Source: Lecture Notes in Computer Science ISBN: 9783031360268
Lecture Notes in Computer Science
Lecture Notes in Computer Science-Computational Science – ICCS 2023

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, 0103 physical sciences, Chaotic System, Convolutional Autoencoder, Reduced Order Modelling, Fluid Dynamics (physics.flu-dyn), FOS: Physical sciences, Physics - Fluid Dynamics, Chaotic Dynamics (nlin.CD), Nonlinear Sciences - Chaotic Dynamics, 01 natural sciences, Machine Learning (cs.LG)

File Description: application/pdf

Access URL: http://arxiv.org/abs/2301.13728
https://link.springer.com/chapter/10.1007/978-3-031-36027-5_24
https://hdl.handle.net/11583/2995102
https://doi.org/10.1007/978-3-031-36027-5_24
http://resolver.tudelft.nl/uuid:8abcbfb3-d741-40dd-959f-95c27759188d