Diffusion Generative Inverse Design

TL;DR

This paper introduces a diffusion-based generative approach for inverse design, significantly reducing simulator calls in fluid dynamics optimization compared to traditional methods.

Contribution

It proposes using denoising diffusion models for efficient inverse design and introduces a particle sampling algorithm to enhance performance.

Findings

Reduces number of simulator calls in design optimization

Outperforms standard techniques in fluid dynamics tasks

Demonstrates efficiency in high-dimensional, non-convex problems

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.