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