  Data-driven simulation is an important step-forward in computational physics
when traditional numerical methods meet their limits. Learning-based simulators
have been widely studied in past years; however, most previous works view
simulation as a general spatial-temporal prediction problem and take little
physical guidance in designing their neural network architectures. In this
paper, we introduce the alternating convolutional Long Short-Term Memory
(Alt-ConvLSTM) that models the force propagation mechanisms in a deformable
object with near-uniform material properties. Specifically, we propose an
accumulation state, and let the network update its cell state and the
accumulation state alternately. We demonstrate how this novel scheme imitates
the alternate updates of the first and second-order terms in the forward Euler
method of numerical PDE solvers. Benefiting from this, our network only
requires a small number of parameters, independent of the number of the
simulated particles, and also retains the essential features in ConvLSTM,
making it naturally applicable to sequential data with spatial inputs and
outputs. We validate our Alt-ConvLSTM on human soft tissue simulation with
thousands of particles and consistent body pose changes. Experimental results
show that Alt-ConvLSTM efficiently models the material kinetic features and
greatly outperforms vanilla ConvLSTM with only the single state update.
