Diffusion Generative Inverse Design
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.
64f82fa8ec913b4f974fc38b
Marin Vlastelica
64f82fa8ec913b4f974fc38c
Tatiana López-Guevara
64f82fa8ec913b4f974fc38d
Kelsey Allen
64f82fa8ec913b4f974fc38e
Peter Battaglia
64f82fa8ec913b4f974fc38f
Arnaud Doucet
64f82fa8ec913b4f974fc390
Kimberley Stachenfeld
Diffusion Generative Inverse Design
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.
Denoising diffusion models combined with a particle sampling algorithm efficiently solve inverse design problems in fluid dynamics with reduced simulator calls.
graph neural networks GNNs differentiable estimation simulator dynamics denoising diffusion models DDMs particle sampling algorithm fluid dynamics inverse design problems