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1 |
Physics-based machine learning for computational fracture mechanics
Aldakheel, Fadi. - Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2026
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2 |
A multiscale CNN-based intrinsic permeability prediction in deformable porous media
Heider, Yousef. - Stuttgart : Universitätsbibliothek der Universität Stuttgart, 2025
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3 |
FE-NN
Stöcker, Julien Philipp. - Dresden : Technische Universität Dresden, 2025
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4 |
An enhanced deep learning approach for vascular wall fracture analysis
Tragoudas, Alexandros. - Hannover : Gottfried Wilhelm Leibniz Universität Hannover, 2024
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5 |
An enhanced deep learning approach for vascular wall fracture analysis
Enthalten in Archive of applied mechanics 15.4.2024: 1-14
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6 |
Efficient multiscale modeling of heterogeneous materials using deep neural networks
Aldakheel, Fadi. - Hannover : Gottfried Wilhelm Leibniz Universität Hannover, 2023
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7 |
FE‐NN: Efficient‐scale transition for heterogeneous microstructures using neural networks
Enthalten in Proceedings in applied mathematics and mechanics 11.09.2023. 8 S.
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8 |
Energy transition with green hydrogen: Toward computational design of comprehensive proton exchange membrane water electrolysis stacks
Enthalten in Proceedings in applied mathematics and mechanics 01.09.2023. 7 S.
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9 |
A Review on Cementitious Self-Healing and the Potential of Phase-Field Methods for Modeling Crack-Closing and Fracture Recovery
Yang, Sha. - Darmstadt : Universitäts- und Landesbibliothek, 2022
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10 |
Bayesian Inversion with Open-Source Codes for Various One-Dimensional Model Problems in Computational Mechanics
Noii, Nima. - Hannover : Gottfried Wilhelm Leibniz Universität Hannover, 2022
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