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Diffusion Generative Inverse Design
Marin Vlastelica · Tatiana Lopez-Guevara · Kelsey Allen · Peter Battaglia · Arnaud Doucet · Kimberly Stachenfeld

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in Structured Probabilistic Inference and Generative Modeling »
Event URL: https://openreview.net/forum?id=SMlQR8mVR4 »

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. Experimentally this approach substantially reduces the number of calls to the simulator compared to standard techniques.

Keywords: guidance · GNNs · diffusion generative modelling · inverse design

Author Information

Marin Vlastelica (Max Planck Institute for Intelligent Systems)
Tatiana Lopez-Guevara (DeepMind)
Kelsey Allen (DeepMind)
Peter Battaglia (DeepMind)
Arnaud Doucet (Oxford University)
Kimberly Stachenfeld (DeepMind)

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