<ul><li>This study investigates different Scientific Machine Learning (SciML) approaches for the analysis of functionally graded porous beams.</li><li>The methods consider the output of a neural network/operator as an approximation to the displacement fields and derive the equations governing beam behavior.</li><li>The study compares three approaches: (a) Physics-Informed Neural Network (PINN), (b) Deep Energy Method (DEM), and (c) Neural Operator methods.</li><li>A neural operator has been trained to predict the response of the porous beam with functionally graded material under any porosity distribution pattern and traction condition.</li></ul>

Applications of Scientific Machine Learning for the Analysis of Functionally Graded Porous Beams

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