  We introduce transfer learning for nonlinear dynamics, which enables
efficient predictions of chaotic dynamics by utilizing a small amount of data.
For the Lorenz chaos, by optimizing the transfer rate, we accomplish more
accurate inference than the conventional method by an order of magnitude.
Moreover, a surprisingly small amount of learning is enough to infer the energy
dissipation rate of the Navier-Stokes turbulence because we can, thanks to the
small-scale universality of turbulence, transfer a large amount of the
knowledge learned from turbulence data at lower Reynolds number.
