  Discovering new physical products and processes often demands enormous
experimentation and expensive simulation. To design a new product with certain
target characteristics, an extensive search is performed in the design space by
trying out a large number of design combinations before reaching to the target
characteristics. However, forward searching for the target design becomes
prohibitive when the target is itself moving or only partially understood. To
address this bottleneck, we propose to use backward prediction by leveraging
the rich data generated during earlier exploration and construct a machine
learning framework to predict the design parameters for any target in a single
step. This poses two technical challenges: the first caused due to one-to-many
mapping when learning the inverse problem and the second caused due to an user
specifying the target specifications only partially. To overcome the
challenges, we formulate this problem as conditional density estimation under
high-dimensional setting with incomplete input and multimodal output. We solve
the problem through a deep hybrid generative-discriminative model, which is
trained end-to-end to predict the optimum design.
