This talk presents new computational approaches for high-dimensional partial differential equations (PDEs), employing tensor networks and convex relaxations. Specifically, based on these approaches, we demonstrate the construction of inner and outer approximations to PDE solutions using low-order statistics. These in turn effectively address the curse of dimensionality.