Physical Design using Differentiable Learned Simulators

• URL: http://arxiv.org/abs/2202.00728v1
• Date: Tue, 1 Feb 2022 19:56:39 GMT
• Title: Physical Design using Differentiable Learned Simulators
• Authors: Kelsey R. Allen, Tatiana Lopez-Guevara, Kimberly Stachenfeld, Alvaro Sanchez-Gonzalez, Peter Battaglia, Jessica Hamrick, Tobias Pfaff
• Abstract summary: In inverse design, learned forward simulators are combined with gradient-based design optimization. This framework produces high-quality designs by propagating through trajectories of hundreds of steps. Our results suggest that despite some remaining challenges, machine learning-based simulators are maturing to the point where they can support general-purpose design optimization.
• Score: 9.380022457753938
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Designing physical artifacts that serve a purpose - such as tools and other functional structures - is central to engineering as well as everyday human behavior. Though automating design has tremendous promise, general-purpose methods do not yet exist. Here we explore a simple, fast, and robust approach to inverse design which combines learned forward simulators based on graph neural networks with gradient-based design optimization. Our approach solves high-dimensional problems with complex physical dynamics, including designing surfaces and tools to manipulate fluid flows and optimizing the shape of an airfoil to minimize drag. This framework produces high-quality designs by propagating gradients through trajectories of hundreds of steps, even when using models that were pre-trained for single-step predictions on data substantially different from the design tasks. In our fluid manipulation tasks, the resulting designs outperformed those found by sampling-based optimization techniques. In airfoil design, they matched the quality of those obtained with a specialized solver. Our results suggest that despite some remaining challenges, machine learning-based simulators are maturing to the point where they can support general-purpose design optimization across a variety of domains.

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