Elsayed Saber Elsayed Ibrahiem Elsayed, M. Sc.
Elsayed Saber Elsayed Ibrahiem Elsayed, M. Sc.
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Appelstraße 9a
30167 Hannover
30167 Hannover
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Elsayed Saber Elsayed Ibrahiem Elsayed, M. Sc.
Research project
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Physics-based machine learning for computational fracture mechanicsIn this project, we plan to develop a physics-based machine learning framework that embeds core physical principles directly into the neural network architecture. Using a feedforward neural network designed to satisfy governing equations and thermodynamic constraints, the framework will integrate mechanics, constitutive behavior, and energy balance at the architectural level. Synthetic data generated from finite element–based phase-field fracture simulations will be used to train the model, initially focusing on homogeneous one-dimensional responses for both brittle and ductile materials. Special emphasis will be placed on learning the evolution of elastic energy, dissipated work, and fracture characteristics in a way that preserves physical interpretability and consistency, addressing key limitations of classical ML approaches that rely purely on data and lack embedded physical guarantees. [...]Led by: Prof. Dr.-Ing. Fadi Aldakheel, Dr.-Ing. Abedulgader BaktheerTeam:Year: 2023
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(2025): Physics-based machine learning for computational fracture mechanics, Mach. Learn. Comput. Sci. Eng 1, 18 More info
DOI: https://doi.org/10.1007/s44379-025-00019-x -
(2024): An enhanced deep learning approach for vascular wall fracture analysis, Archive of Applied Mechanics, Pages 1-14 More info
DOI: https://doi.org/10.1007/s00419-024-02589-3 -
(2023): FE‐NN: Efficient‐scale transition for heterogeneous microstructures using neural networks, PAMM, e202300011 More info
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(2023): Efficient multiscale modeling of heterogeneous materials using deep neural networks, Springer Berlin Heidelberg, Computational Mechanics, Vol. 72, 155-171 More info
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(2023): Machine learning aided multiscale magnetostatics, Sciencedirect, Mechanics of Materials, Vol. 184, 104726 More info
