Dr. Marwa Ghazi Karim, a faculty member, published her research paper titled “Mechanical-Informed Hybrid Machine Learning Models for Predicting Mechanical Failure in Graphene Sponges: A Data-Constrained Strategy for Mechanical Engineering Applications” in the prestigious international journal Advanced Engineering Applications. The study addressed the development of a hybrid approach that integrates physical measurements and artificial intelligence techniques to accurately predict the mechanical failure strength of three-dimensional graphene sponges. Graphene sponges are nanomaterials with a complex porous structure that are difficult to model traditionally due to the overlap of their structural planes and the limited availability of experimental data.
The results showed the remarkable superiority of the GPR model, achieving a coefficient of determination (R^2) of 0.967 and an average absolute error of 0.192 MPa. This demonstrates the ability of physics-informed AI-based models to provide accurate and productive mechanical predictions under data-constrained strategies, thus enhancing intelligent design methodologies for nanostructured materials. Advanced Engineering Applications
It is worth noting that the journal Advanced Engineering Applications is ranked in the second quartile (Q2) in Clarivate and Scopus and is published by De Gruyter.
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