Harnessing Machine Learning in Physics Simulations
Summary
Neural networks, inspired by the human brain’s structure, are a subset of machine learning that excel at recognizing patterns and making predictions from large datasets. When applied to structural design simulations, neural networks offer significant advantages such as accelerated computational speed and freeing up resources for other important tasks. To illustrate the process, a linear static analysis was performed using SOLIDWORKS Simulation to evaluate stresses in press assembly subjected to force. The top loading of a plastic bottle was simulated using nonlinear dynamics analysis that applied a prescribed displacement and evaluated reaction force. The training data for the neural network was generated by solving several simulations that calculated the reaction force for various thickness values and shapes of the bottle.