MACHINE LEARNING AND NEURAL NETWORKS BASED APPROACH FOR DEFLECTION PREDICTION OF EULER-BERNOULLI BEAM EQUATIONS

Abstract

Beam-like structures are widespread but essential systems that have been extensively studied for centuries. Several proposed solutions have been effective, but not all are suitable because they are timeconsuming and lack accuracy. This paper offers a new methodology for finding solutions to beam problems based on machine learning and neural networks with different optimization algorithms. Different regression models are compared on numerically stimulated Euler-Bernoulli beam modelling.