In the example documentation of PINO, it uses stationary PDE(input tensor shape: 1024,1,421,421). But my PDE depends on time, so my input tensor shape is (1024,100,421,421).

Can you kindly guide me how to solve dynamical PDE in PINO?

We presently do not have an example of PINO for a transient problem, just 2D steady state. You could predict all time-steps at once with a 4D output and approximate time derivatives using finite difference, Fourier derivatives, etc. in that dimension.

One approach I would consider is an auto-regressive approach which can work well for physical systems with physics based loss functions. For data-driven this is how models like FourCastNet (Adaptive FNO under the hood) operate.