Diffusion Generative Inverse Design

• URL: http://arxiv.org/abs/2309.02040v2
• Date: Mon, 18 Sep 2023 08:01:50 GMT
• Title: Diffusion Generative Inverse Design
• Authors: Marin Vlastelica, Tatiana L\'opez-Guevara and Kelsey Allen, Peter Battaglia, Arnaud Doucet, Kimberley Stachenfeld
• Abstract summary: Inverse design refers to the problem of optimizing the input of an objective function in order to enact a target outcome. Recent developments in learned graph neural networks (GNNs) can be used for accurate, efficient, differentiable estimation of simulator dynamics. We show how denoising diffusion diffusion models can be used to solve inverse design problems efficiently and propose a particle sampling algorithm for further improving their efficiency.
• Score: 28.04683283070957
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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