Deep learning-based modeling and solution method for nonlinear optimization problems in biomechanics
Abstract
In order to cope with biomechanical nonlinear optimization problems and explore the application of deep learning methods, the study focuses on the performance of neural network-based optimization models in complex biomechanical systems. By using a hybrid neural network structure, the optimization algorithm processes high-dimensional data to accurately model biomechanical nonlinear relationships. The experimental results show that the deep learning model shows significant improvement in multivariate biomechanics prediction compared to traditional methods, with the prediction error decreasing to less than 10% and the optimization efficiency increasing by more than 40%. Especially in the field of joint mechanics and skeletal implant design, deep learning is able to accurately capture complex nonlinear laws, which greatly improves the stability and reliability of the results.
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