Physics-informed neural networks reproduce Saint-Venant torsion solutions for 2D cross-sections and 1D sharp-transition problems, with reported relative errors as low as 0.1% for simple shapes.
Prandtl’s formulation for the saint--venant’s torsion of homogeneous piezoelectric beams
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Engineering application of physics-informed neural networks for Saint-Venant torsion
Physics-informed neural networks reproduce Saint-Venant torsion solutions for 2D cross-sections and 1D sharp-transition problems, with reported relative errors as low as 0.1% for simple shapes.