  Neural networks are powerful surrogates for numerous forward processes. The
inversion of such surrogates is extremely valuable in science and engineering.
The most important property of a successful neural inverse method is the
performance of its solutions when deployed in the real world, i.e., on the
native forward process (and not only the learned surrogate). We propose
Autoinverse, a highly automated approach for inverting neural network
surrogates. Our main insight is to seek inverse solutions in the vicinity of
reliable data which have been sampled form the forward process and used for
training the surrogate model. Autoinverse finds such solutions by taking into
account the predictive uncertainty of the surrogate and minimizing it during
the inversion. Apart from high accuracy, Autoinverse enforces the feasibility
of solutions, comes with embedded regularization, and is initialization free.
We verify our proposed method through addressing a set of real-world problems
in control, fabrication, and design.
