Diffusion Generative Inverse Design
Authors: name: Marin Vlastelica, name: Tatiana López-Guevara, name: Kelsey Allen, name: Peter Battaglia, name: Arnaud Doucet, name: Kimberley Stachenfeld

Inverse design refers to the problem of optimizing the input of an objective
function in order to enact a target outcome. For many real-world engineering
problems, the objective function takes the form of a simulator that predicts
how the system state will evolve over time, and the design challenge is to
optimize the initial conditions that lead to a target outcome. Recent
developments in learned simulation have shown that graph neural networks (GNNs)
can be used for accurate, efficient, differentiable estimation of simulator
dynamics, and support high-quality design optimization with gradient- or
sampling-based optimization procedures. However, optimizing designs from
scratch requires many expensive model queries, and these procedures exhibit
basic failures on either non-convex or high-dimensional problems.In this work,
we show how denoising diffusion models (DDMs) can be used to solve inverse
design problems efficiently and propose a particle sampling algorithm for
further improving their efficiency. We perform experiments on a number of fluid
dynamics design challenges, and find that our approach substantially reduces
the number of calls to the simulator compared to standard techniques.