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1
Thermodynamics-based Artificial Neural Networks for constitutive modeling
Thermodynamics-based Artificial Neural Networks for constitutive modeling
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Thermodynamics-based Artificial Neural Networks for constitutive modeling

Journal of the mechanics and physics of solids, 2021-02, Vol.147 [Peer Reviewed Journal]

Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0022-5096 ;DOI: 10.1016/j.jmps.2020.104277

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2
Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil
Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil
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Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil

Mathematical problems in engineering, 2021, Vol.2021, p.1-15 [Peer Reviewed Journal]

Copyright © 2021 Quang Hung Nguyen et al. ;COPYRIGHT 2021 Hindawi Limited ;Copyright © 2021 Quang Hung Nguyen et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 ;ISSN: 1024-123X ;ISSN: 1563-5147 ;EISSN: 1563-5147 ;DOI: 10.1155/2021/4832864

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3
Chemistry reduction using machine learning trained from non-premixed micro-mixing modeling: Application to DNS of a syngas turbulent oxy-flame with side-wall effects
Chemistry reduction using machine learning trained from non-premixed micro-mixing modeling: Application to DNS of a syngas turbulent oxy-flame with side-wall effects
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Chemistry reduction using machine learning trained from non-premixed micro-mixing modeling: Application to DNS of a syngas turbulent oxy-flame with side-wall effects

Combustion and flame, 2020-10, Vol.220, p.119-129 [Peer Reviewed Journal]

Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0010-2180 ;EISSN: 1556-2921 ;DOI: 10.1016/j.combustflame.2020.06.008

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4
Research of Intelligent Video Surveillance System based on Artificial Neural Network
Research of Intelligent Video Surveillance System based on Artificial Neural Network
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Research of Intelligent Video Surveillance System based on Artificial Neural Network

Journal of physics. Conference series, 2022-01, Vol.2181 (1), p.12057 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;Published under licence by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1742-6588 ;EISSN: 1742-6596 ;DOI: 10.1088/1742-6596/2181/1/012057

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5
Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection
Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection
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Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection

Remote sensing (Basel, Switzerland), 2019-01, Vol.11 (2), p.196 [Peer Reviewed Journal]

2019. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs11020196

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6
Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science
Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science
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Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science

Nature communications, 2018-06, Vol.9 (1), p.2383-12, Article 2383 [Peer Reviewed Journal]

2018. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2018 ;ISSN: 2041-1723 ;EISSN: 2041-1723 ;DOI: 10.1038/s41467-018-04316-3 ;PMID: 29921910

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7
A graph-convolutional neural network model for the prediction of chemical reactivity
A graph-convolutional neural network model for the prediction of chemical reactivity
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A graph-convolutional neural network model for the prediction of chemical reactivity

Chemical science (Cambridge), 2019-01, Vol.10 (2), p.370-377 [Peer Reviewed Journal]

Copyright Royal Society of Chemistry 2019 ;ISSN: 2041-6520 ;EISSN: 2041-6539 ;DOI: 10.1039/c8sc04228d ;PMID: 30746086

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8
Training deep quantum neural networks
Training deep quantum neural networks
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Training deep quantum neural networks

Nature communications, 2020-02, Vol.11 (1), p.808-808, Article 808 [Peer Reviewed Journal]

This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2020 ;ISSN: 2041-1723 ;EISSN: 2041-1723 ;DOI: 10.1038/s41467-020-14454-2 ;PMID: 32041956

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9
E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
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E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

Nature communications, 2022-05, Vol.13 (1), p.2453-2453, Article 2453 [Peer Reviewed Journal]

2022. The Author(s). ;The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2022 ;ISSN: 2041-1723 ;EISSN: 2041-1723 ;DOI: 10.1038/s41467-022-29939-5 ;PMID: 35508450

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10
1D convolutional neural networks and applications: A survey
1D convolutional neural networks and applications: A survey
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1D convolutional neural networks and applications: A survey

Mechanical systems and signal processing, 2021-04, Vol.151 [Peer Reviewed Journal]

ISSN: 0888-3270 ;ISSN: 1096-1216 ;EISSN: 1096-1216 ;DOI: 10.1016/j.ymssp.2020.107398

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11
Fully hardware-implemented memristor convolutional neural network
Fully hardware-implemented memristor convolutional neural network
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Fully hardware-implemented memristor convolutional neural network

Nature (London), 2020-01, Vol.577 (7792), p.641-646 [Peer Reviewed Journal]

COPYRIGHT 2020 Nature Publishing Group ;COPYRIGHT 2020 Nature Publishing Group ;Copyright Nature Publishing Group Jan 30, 2020 ;ISSN: 0028-0836 ;EISSN: 1476-4687 ;DOI: 10.1038/s41586-020-1942-4 ;PMID: 31996818

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12
Benign overfitting in linear regression
Benign overfitting in linear regression
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Benign overfitting in linear regression

Proceedings of the National Academy of Sciences - PNAS, 2020-12, Vol.117 (48), p.30063-30070 [Peer Reviewed Journal]

Copyright National Academy of Sciences Dec 1, 2020 ;2020 ;ISSN: 0027-8424 ;EISSN: 1091-6490 ;DOI: 10.1073/pnas.1907378117 ;PMID: 32332161

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13
On explaining the surprising success of reservoir computing forecaster of chaos? The universal machine learning dynamical system with contrast to VAR and DMD
On explaining the surprising success of reservoir computing forecaster of chaos? The universal machine learning dynamical system with contrast to VAR and DMD
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On explaining the surprising success of reservoir computing forecaster of chaos? The universal machine learning dynamical system with contrast to VAR and DMD

Chaos (Woodbury, N.Y.), 2021-01, Vol.31 (1), p.013108-013108 [Peer Reviewed Journal]

Author(s) ;2021 Author(s). Published under license by AIP Publishing. ;ISSN: 1054-1500 ;EISSN: 1089-7682 ;DOI: 10.1063/5.0024890 ;PMID: 33754755 ;CODEN: CHAOEH

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14
Deep physical neural networks trained with backpropagation
Deep physical neural networks trained with backpropagation
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Deep physical neural networks trained with backpropagation

Nature (London), 2022-01, Vol.601 (7894), p.549-555 [Peer Reviewed Journal]

2022. The Author(s). ;Copyright Nature Publishing Group Jan 27, 2022 ;The Author(s) 2022 ;ISSN: 0028-0836 ;EISSN: 1476-4687 ;DOI: 10.1038/s41586-021-04223-6 ;PMID: 35082422

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15
Accelerating gradient-based topology optimization design with dual-model artificial neural networks
Accelerating gradient-based topology optimization design with dual-model artificial neural networks
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Accelerating gradient-based topology optimization design with dual-model artificial neural networks

Structural and multidisciplinary optimization, 2021-04, Vol.63 (4), p.1687-1707 [Peer Reviewed Journal]

Springer-Verlag GmbH Germany, part of Springer Nature 2020 ;Springer-Verlag GmbH Germany, part of Springer Nature 2020. ;ISSN: 1615-147X ;EISSN: 1615-1488 ;DOI: 10.1007/s00158-020-02770-6

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16
Implementation of machine-learning classification in remote sensing: an applied review
Implementation of machine-learning classification in remote sensing: an applied review
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Implementation of machine-learning classification in remote sensing: an applied review

International journal of remote sensing, 2018-05, Vol.39 (9), p.2784-2817 [Peer Reviewed Journal]

2018 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. 2018 ;ISSN: 0143-1161 ;EISSN: 1366-5901 ;DOI: 10.1080/01431161.2018.1433343

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17
Learning Explanatory Rules from Noisy Data
Learning Explanatory Rules from Noisy Data
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Learning Explanatory Rules from Noisy Data

The Journal of artificial intelligence research, , Vol.61, p.1-64 [Peer Reviewed Journal]

2018. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the associated terms available at https://www.jair.org/index.php/jair/about ;ISSN: 1076-9757 ;EISSN: 1943-5037 ;DOI: 10.1613/jair.5714

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18
Generalization in quantum machine learning from few training data
Generalization in quantum machine learning from few training data
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Generalization in quantum machine learning from few training data

Nature communications, 2022-08, Vol.13 (1), p.4919-4919, Article 4919 [Peer Reviewed Journal]

The Author(s) 2022. corrected publication 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2022 ;ISSN: 2041-1723 ;EISSN: 2041-1723 ;DOI: 10.1038/s41467-022-32550-3 ;PMID: 35995777

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19
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
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SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

IEEE transactions on pattern analysis and machine intelligence, 2017-12, Vol.39 (12), p.2481-2495 [Peer Reviewed Journal]

ISSN: 0162-8828 ;EISSN: 1939-3539 ;DOI: 10.1109/TPAMI.2016.2644615 ;PMID: 28060704 ;CODEN: ITPIDJ

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20
Comparative Traffic Flow Prediction of a Heuristic ANN Model and a Hybrid ANN-PSO Model in the Traffic Flow Modelling of Vehicles at a Four-Way Signalized Road Intersection
Comparative Traffic Flow Prediction of a Heuristic ANN Model and a Hybrid ANN-PSO Model in the Traffic Flow Modelling of Vehicles at a Four-Way Signalized Road Intersection
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Comparative Traffic Flow Prediction of a Heuristic ANN Model and a Hybrid ANN-PSO Model in the Traffic Flow Modelling of Vehicles at a Four-Way Signalized Road Intersection

Sustainability, 2021-10, Vol.13 (19), p.10704 [Peer Reviewed Journal]

2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2071-1050 ;EISSN: 2071-1050 ;DOI: 10.3390/su131910704

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