Applications of Scientific Machine Learning for the Analysis of Functionally Graded Porous Beams
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Abstract (arXiv)
This study investigates different Scientific Machine Learning (SciML) approaches for the analysis of functionally graded (FG) porous beams and compares them under a new framework. The beam material properties are assumed to vary as an arbitrary continuous function. The methods consider the output of a neural network/operator as an approximation to the displacement fields and derive the equations governing beam behavior based on the continuum formulation. The methods are implemented in the framework and formulated by three approaches: (a) the vector approach leads to a Physics-Informed Neural Network (PINN), (b) the energy approach brings about the Deep Energy Method (DEM), and (c) the data-driven approach, which results in a class of Neural Operator methods. Finally, a neural operator has been trained to predict the response of the porous beam with functionally graded material under any porosity distribution pattern and any arbitrary traction condition. The results are validated with analytical and numerical reference solutions. The data and code accompanying this manuscript will be publicly available at https://github.com/eshaghi-ms/DeepNetBeam.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 21 of its 70 references that are in the map. First posted on arXiv 4 Aug 2024.
Builds on
21 of 70 references.
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.