Latent Task-Specific Graph Network Simulators

Philipp Dahlinger, Niklas Freymuth, Tai Hoang, Michael Volpp, Gerhard Neumann

Keywords: Graph Network Simulators, Graph Neural Networks, Meta-Learning, Neural Processes, Deformable Object Simulation, MeshGraphNets

TL;DR: We introduce a latent task-specific Graph Network Simulator, which improves over existing learned simulators by framing mesh-based simulation as a meta-learning problem.

Abstract:

Simulating object deformations is a critical challenge in many scientific domains, with applications ranging from robotics to materials science. Learned Graph Network Simulators (GNSs) are an efficient alternative to traditional mesh-based physics simulators. Their speed and inherent differentiability make them particularly well-suited for inverse design problems such as process optimization. However, these applications typically offer limited available data, making GNSs difficult to use in real-world scenarios. We frame mesh-based simulation as a meta-learning problem and apply conditional Neural Processes to adapt to new simulation scenarios with little data. In addition, we address the problem of error accumulation common in previous step-based methods by combining this approach with movement primitives, allowing efficient predictions of full trajectories. We validate the effectiveness of our approach, called Movement-primitive Meta-MeshGraphNet (M3GN), through a variety of experiments, outperforming state-of-the-art step-based baseline GNSs and step-based meta-learning methods.

Primary Area: learning on graphs and other geometries & topologies

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Submission Number: 6180