  Deep learning became the method of choice in recent year for solving a wide
variety of predictive analytics tasks. For sequence prediction, recurrent
neural networks (RNN) are often the go-to architecture for exploiting
sequential information where the output is dependent on previous computation.
However, the dependencies of the computation lie in the latent domain which may
not be suitable for certain applications involving the prediction of a
step-wise transformation sequence that is dependent on the previous computation
only in the visible domain. We propose that a hybrid architecture of
convolution neural networks (CNN) and stacked autoencoders (SAE) is sufficient
to learn a sequence of actions that nonlinearly transforms an input shape or
distribution into a target shape or distribution with the same support. While
such a framework can be useful in a variety of problems such as robotic path
planning, sequential decision-making in games, and identifying material
processing pathways to achieve desired microstructures, the application of the
framework is exemplified by the control of fluid deformations in a microfluidic
channel by deliberately placing a sequence of pillars. Learning of a multistep
topological transform has significant implications for rapid advances in
material science and biomedical applications.
