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Reinforcement Learning Based, Staircase Negotiation Learning: Simulation and Transfer to Reality for Articulated Tracked Robots
Reinforcement Learning Based, Staircase Negotiation Learning: Simulation and Transfer to Reality for Articulated Tracked Robots
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Reinforcement Learning Based, Staircase Negotiation Learning: Simulation and Transfer to Reality for Articulated Tracked Robots

Mitriakov, Andrei ; Papadakis, Panagiotis ; Kerdreux, Jerome ; Garlatti, Serge

IEEE robotics & automation magazine, 2021-12, Vol.28 (4), p.10-20 [Periódico revisado por pares]

New York: IEEE

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2
Revisiting the nodal voltage method for both human comprehension and software implementation: Towards a teaching/self‐learning simulation tool
Revisiting the nodal voltage method for both human comprehension and software implementation: Towards a teaching/self‐learning simulation tool
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Revisiting the nodal voltage method for both human comprehension and software implementation: Towards a teaching/self‐learning simulation tool

Sousa, Lino ; Rocha, André ; Alves, Mário ; Pereira, Francisco

Computer applications in engineering education, 2021-11, Vol.29 (6), p.1642-1664 [Periódico revisado por pares]

Hoboken: Wiley Subscription Services, Inc

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3
Simulation-assisted machine learning
Simulation-assisted machine learning
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Simulation-assisted machine learning

Deist, Timo M ; Patti, Andrew ; Wang, Zhaoqi ; Krane, David ; Sorenson, Taylor ; Craft, David Stegle, Oliver

Bioinformatics, 2019-10, Vol.35 (20), p.4072-4080 [Periódico revisado por pares]

England: Oxford University Press

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4
Learning to bag with a simulation‐free reinforcement learning framework for robots
Learning to bag with a simulation‐free reinforcement learning framework for robots
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Learning to bag with a simulation‐free reinforcement learning framework for robots

Munguia‐Galeano, Francisco ; Zhu, Jihong ; Hernández, Juan David ; Ji, Ze

IET cyber-systems and robotics, 2024-06, Vol.6 (2), p.n/a [Periódico revisado por pares]

Wiley

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5
Real-time digital twin-based optimization with predictive simulation learning
Real-time digital twin-based optimization with predictive simulation learning
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Real-time digital twin-based optimization with predictive simulation learning

Goodwin, Travis ; Xu, Jie ; Celik, Nurcin ; Chen, Chun-Hung

Journal of simulation : JOS, 2024-01, Vol.ahead-of-print (ahead-of-print), p.1-18 [Periódico revisado por pares]

Taylor & Francis

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6
Parameter Control of Genetic Algorithms by Learning and Simulation of Bayesian Networks -- A Case Study for the Optimal Ordering of Tables
Parameter Control of Genetic Algorithms by Learning and Simulation of Bayesian Networks -- A Case Study for the Optimal Ordering of Tables
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Parameter Control of Genetic Algorithms by Learning and Simulation of Bayesian Networks -- A Case Study for the Optimal Ordering of Tables

Bielza, Concha ; Fernández del Pozo, Juan A ; Larrañaga, Pedro

Journal of computer science and technology, 2013-07, Vol.28 (4), p.720-731 [Periódico revisado por pares]

Boston: Springer US

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7
Transfer Learning From Simulations on a Reference Anatomy for ECGI in Personalized Cardiac Resynchronization Therapy
Transfer Learning From Simulations on a Reference Anatomy for ECGI in Personalized Cardiac Resynchronization Therapy
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Transfer Learning From Simulations on a Reference Anatomy for ECGI in Personalized Cardiac Resynchronization Therapy

Giffard-Roisin, Sophie ; Sermesant, Maxime ; Delingette, Herve ; Jackson, Thomas ; Webb, Jessica ; Fovargue, Lauren ; Lee, Jack ; Rinaldi, Christopher A. ; Razavi, Reza ; Ayache, Nicholas

IEEE transactions on biomedical engineering, 2019-02, Vol.66 (2), p.343-353 [Periódico revisado por pares]

United States: IEEE

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8
Caffeoyl malic acid is a potential dual inhibitor targeting TNFα/IL-4 evaluated by a combination strategy of network analysis-deep learning-molecular simulation
Caffeoyl malic acid is a potential dual inhibitor targeting TNFα/IL-4 evaluated by a combination strategy of network analysis-deep learning-molecular simulation
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Caffeoyl malic acid is a potential dual inhibitor targeting TNFα/IL-4 evaluated by a combination strategy of network analysis-deep learning-molecular simulation

Wang, Yue ; Qin, Dongya ; Jin, Liang ; Liang, Guizhao

Computers in biology and medicine, 2022-06, Vol.145, p.105410-105410, Article 105410 [Periódico revisado por pares]

United States: Elsevier Ltd

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9
Effects of learning support in simulation-based physics learning
Effects of learning support in simulation-based physics learning
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Effects of learning support in simulation-based physics learning

Chang, Kuo-En ; Chen, Yu-Lung ; Lin, He-Yan ; Sung, Yao-Ting

Computers and education, 2008-12, Vol.51 (4), p.1486-1498 [Periódico revisado por pares]

Elsevier Ltd

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10
Investigating teachers' enactment of engineering design practices in a CAD simulation‐enhanced learning environment
Investigating teachers' enactment of engineering design practices in a CAD simulation‐enhanced learning environment
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Investigating teachers' enactment of engineering design practices in a CAD simulation‐enhanced learning environment

Dasgupta, Chandan ; Magana, Alejandra J. ; Kirkham, Lisa

Computer applications in engineering education, 2021-11, Vol.29 (6), p.1465-1479 [Periódico revisado por pares]

Hoboken: Wiley Subscription Services, Inc

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