Comparative Analysis of Physics-Informed Semi-Empirical Neural Network Bending Models of a Real Cantilever Beam
Abstract
In the paper, we study the use of neural network modeling to solve problems of a sufficiently accurate description of a specific object considering its approximate physical model and measurement data. Nonparametric and parametric networks are applied. Different approaches to the construction of physics-informed neural networks (PINN) based on heterogeneous information are compared. The best results were obtained for new models based on the author's methodology for constructing multilayer functional models and analytical modification of numerical methods (AMNM). The results of calculations are presented, and their analysis is given.
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