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Dipendra Jha

Showing results (1-10 of 7) with videos related to

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Scientific Reports|July 13, 2022
Moving closer to experimental level materials property prediction using AIDipendra Jha, Vishu Gupta, Wei-Keng Liao, et al.
Scientific Reports|December 6, 2018
ElemNet: Deep Learning the Chemistry of Materials From Only Elemental CompositionDipendra Jha, Logan Ward, Arindam Paul, et al.
Nature Communications|November 24, 2019
Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learningDipendra Jha, Kamal Choudhary, Francesca Tavazza, et al.
Nature Communications|July 17, 2020
Author Correction: Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learningDipendra Jha, Kamal Choudhary, Francesca Tavazza, et al.
Integrating Materials and Manufacturing Innovation|December 19, 2022
Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials DesignYuwei Mao, Zijiang Yang, Dipendra Jha, et al.
Microscopy and Microanalysis : the Official Journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada|October 19, 2018
Extracting Grain Orientations from EBSD Patterns of Polycrystalline Materials Using Convolutional Neural NetworksDipendra Jha, Saransh Singh, Reda Al-Bahrani, et al.
Scientific Reports|February 20, 2021
Enabling deeper learning on big data for materials informatics applicationsDipendra Jha, Vishu Gupta, Logan Ward, et al.
Pageof 1

Showing results (1-10 of 7) with videos related to

Sort By:
Pageof 1
Scientific Reports|July 13, 2022
Moving closer to experimental level materials property prediction using AIDipendra Jha, Vishu Gupta, Wei-Keng Liao, et al.
Scientific Reports|December 6, 2018
ElemNet: Deep Learning the Chemistry of Materials From Only Elemental CompositionDipendra Jha, Logan Ward, Arindam Paul, et al.
Nature Communications|November 24, 2019
Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learningDipendra Jha, Kamal Choudhary, Francesca Tavazza, et al.
Nature Communications|July 17, 2020
Author Correction: Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learningDipendra Jha, Kamal Choudhary, Francesca Tavazza, et al.
Integrating Materials and Manufacturing Innovation|December 19, 2022
Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials DesignYuwei Mao, Zijiang Yang, Dipendra Jha, et al.
Microscopy and Microanalysis : the Official Journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada|October 19, 2018
Extracting Grain Orientations from EBSD Patterns of Polycrystalline Materials Using Convolutional Neural NetworksDipendra Jha, Saransh Singh, Reda Al-Bahrani, et al.
Scientific Reports|February 20, 2021
Enabling deeper learning on big data for materials informatics applicationsDipendra Jha, Vishu Gupta, Logan Ward, et al.
Pageof 1