This chapter discusses two topics related to computation using the multiblock method. The first topic is the artificial odd–even oscillation that frequently occurs in numerical solutions. The chapter starts with the computation of a model equation on a single grid. It identifies the odd–even oscillation resulting from two causes: a dual-mode solution and inconsistent boundary conditions. The chapter presents criteria for the onset of the oscillation, followed by their illustration through numerical experiments. Then, the chapter proceeds to the computation on two grids, presenting criteria for its onset and numerical examples. Finally, it discusses the odd–even oscillation associated with more complicated equations. The second topic concerns coupling numerical solutions of differential equations at subdomain interfaces by machine learning (ML), representing a potentially novel paradigm and an exploratory study. The basic idea of ML coupling is to utilize ML models to compute subdomain interface conditions. This chapter outlines the concepts, approaches, and algorithms for the ML coupling. It begins with a boundary value problem of the Poisson equation. Then, the chapter extends the coupling to an initial value problem of parabolic equations and proposes an ML Godunov method to conduct the ML coupling. Numerical examples demonstrate the promising performance of the ML coupling. To further illustrate the promise, a preliminary numerical result is presented for an ML solution in the interface zone of a cavity flow.