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

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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