Universal machine learning for topology optimization

We put forward a general machine learning-based topology optimization framework, which greatly accelerates the design process of large-scale problems, without sacrifice in accuracy. The proposed framework has three distinguishing features. First, a novel online training concept is established using data from earlier iterations of the topology optimization process. Thus, the training is done during, rather than before, the topology optimization. Second, a tailored two-scale topology optimization formulation is adopted, which introduces a localized online training strategy. This training strategy can improve both the scalability and accuracy of the proposed framework. Third, an online updating scheme is synergistically incorporated, which continuously improves the prediction accuracy of the machine learning models by providing new data generated from actual physical simulations. Through numerical investigations and design examples, we demonstrate that the aforementioned framework is highly scalable and can efficiently handle design problems with a wide range of discretization levels, different load and boundary conditions, and various design considerations (e.g., the presence of non-designable regions).