  Soft deployable structures - unlike conventional piecewise rigid deployables
based on hinges and springs - can assume intricate 3-D shapes, thereby enabling
transformative technologies in soft robotics, shape-morphing architecture, and
pop-up manufacturing. Their virtually infinite degrees of freedom allow precise
control over the final shape. The same enabling high dimensionality, however,
poses a challenge for solving the inverse design problem involving this class
of structures: to achieve desired 3D structures it typically requires
manufacturing technologies with extensive local actuation and control during
fabrication, and a trial and error search over a large design space. We address
both of these shortcomings by first developing a simplified planar fabrication
approach that combines two ingredients: strain mismatch between two layers of a
composite shell and kirigami cuts that relieves localized stress. In principle,
it is possible to generate targeted 3-D shapes by designing the appropriate
kirigami cuts and selecting the right amount of prestretch, thereby eliminating
the need for local control. Second, we formulate a data-driven physics-guided
framework that reduces the dimensionality of the inverse design problem using
autoencoders and efficiently searches through the ``latent" parameter space in
an active learning approach. We demonstrate the effectiveness of the rapid
design procedure via a range of target shapes, such as peanuts, pringles,
flowers, and pyramids. Tabletop experiments are conducted to fabricate the
target shapes. Experimental results and numerical predictions from our
framework are found to be in good agreement.
