Research on Applications Of Machine Learning In Mechanics

Inhalt:
Machine learning is a technology that becomes more and more popular nowadays. Many physics researchers discovered its usages and advantages, then applied it to different models and obtained some achievements. This thesis introduces some papers’ examples of using machine learning to solve physics problems, including double pendulum, conservation law, time series on chaotic systems, and it demonstrates the feasibility of machine learning on some mechanics projects by analyzing different application methods mentioned in those papers. The result of the experiments shows machine learning’s good behaviors in this area, most of the models that used machine learning are successful. Overall, machine learning technology has great potential and features to do more contributions for solving mechanics and physics problems.