  Nonlinear system design is often a multi-objective optimization problem
involving search for a design that satisfies a number of predefined
constraints. The design space is typically very large since it includes all
possible system architectures with different combinations of components
composing each architecture. In this article, we address nonlinear system
design space exploration through a two-step approach encapsulated in a
framework called Fast Design Space Exploration of Nonlinear Systems (ASSENT).
In the first step, we use a genetic algorithm to search for system
architectures that allow discrete choices for component values or else only
component values for a fixed architecture. This step yields a coarse design
since the system may or may not meet the target specifications. In the second
step, we use an inverse design to search over a continuous space and fine-tune
the component values with the goal of improving the value of the objective
function. We use a neural network to model the system response. The neural
network is converted into a mixed-integer linear program for active learning to
sample component values efficiently. We illustrate the efficacy of ASSENT on
problems ranging from nonlinear system design to design of electrical circuits.
Experimental results show that ASSENT achieves the same or better value of the
objective function compared to various other optimization techniques for
nonlinear system design by up to 54%. We improve sample efficiency by 6-10x
compared to reinforcement learning based synthesis of electrical circuits.

Systems and Control;Artificial Intelligence;Machine Learning;Neural and Evolutionary Computing;Systems and Control