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

A practical existence theorem for reduced order models based on convolutional autoencoders

Authors: Franco, Nicola Rares Brugiapaglia, Simone

Source: Foundations of Data Science. 7:72-98

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Numerical approximation of high-dimensional functions, sparse grids, Numerical Analysis (math.NA), Theoretical approximation in context of PDEs, 01 natural sciences, parametrized PDEs, Machine Learning (cs.LG), convolutional autoencoders, Artificial Intelligence (cs.AI), FOS: Mathematics, Mathematics - Numerical Analysis, 0101 mathematics, approximation theory, reduced order modeling, Artificial neural networks and deep learning, neural network training

File Description: application/xml

Access URL: http://arxiv.org/abs/2402.00435

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

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

Authors: Tianxuan Hao Lizhen Zhao Yang Du et al.

Source: Information, Vol 15, Iss 11, p 733 (2024)

Subject Terms: coal mine, gas extraction, gas flow model, reduced-order model, deep neural networks, convolutional autoencoders, Information technology, T58.5-58.64

File Description: electronic resource

Relation: https://www.mdpi.com/2078-2489/15/11/733; https://doaj.org/toc/2078-2489

Access URL: https://doaj.org/article/cea1d6b7ceb442ee919d8fa3a8d5c4a6

Combining Extended Convolutional Autoencoders and Reservoir Computing for Accurate Reduced-Order Predictions of Atmospheric Flows

Authors: Hajisharifi, Arash Girfoglio, Michele Quaini, Annalisa et al.

Subject Terms: FOS: Mathematics, Mathematics - Numerical Analysis, Numerical Analysis (math.NA)

Access URL: http://arxiv.org/abs/2504.01097

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

Non‐intrusive reduced‐order modeling using convolutional autoencoders

Authors: Rakesh Halder Krzysztof J. Fidkowski Kevin J. Maki

Source: International Journal for Numerical Methods in Engineering. 123:5369-5390

Subject Terms: 0301 basic medicine, 0303 health sciences, Mechanical Engineering, deep learning, Numerical Analysis (math.NA), 03 medical and health sciences, proper orthogonal decomposition, machine learning, Engineering, autoencoders, model reduction, Engineering (General), FOS: Mathematics, non-intrusive, Mathematics - Numerical Analysis

File Description: application/pdf

Access URL: http://arxiv.org/abs/2204.08280

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

A practical existence theorem for reduced order models based on convolutional autoencoders

Authors: Franco, Nicola Rares Brugiapaglia, Simone

Index Terms: Mathematics - Numerical Analysis, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/2402.00435

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

Correction: Reduced-Order Representation and Effective Property Prediction of CNT-polymer nanocomposites using Convolutional Neural Networks and Autoencoders

Authors: Kavan Shah Gary Seidel

Source: AIAA SCITECH 2024 Forum.

Reduced-Order Representation and Effective Property Prediction of CNT-polymer nanocomposites using Convolutional Neural Networks and Autoencoders

Authors: Kavan Shah Gary Seidel

Source: AIAA SCITECH 2024 Forum.