MIT PhD Thesis, 2018
Modern manufacturing technologies such as 3D printing enable the fabrication of objects with extraordinary complexity. Arranging materials to form functional structures can achieve a much wider range of physical properties than in the constituent materials. Many applications have been demonstrated in the fields of mechanics, acoustics, optics, and electromagnetics. Unfortunately, it is difficult to design objects manually in the large combinatorial space of possible designs. Computational design algorithms have been developed to automatically design objects with specified physical properties. However, many types of physical properties are still very challenging to optimize because predictive and efficient simulations are not available for problems such as high-resolution non-linear elasticity or dynamics with friction and impact. For simpler problems such as linear elasticity, where accurate simulation is available, the simulation resolution handled by desktop workstations is still orders of magnitudes below available printing resolutions. We propose to speed up simulation and inverse design process of fabricable objects by using multiscale methods. Our method computes coarse-scale simulation meshes with datadrive material models. It improves the simulation efficiency while preserving the characteristic deformation and motion of elastic objects.