Simulating dynamic physical interactions is a critical challenge across multiple scientific domains, with applications ranging from robotics to material science. For mesh-based simulations, Graph Network Simulators (GNSs) pose an efficient alternative to traditional physics-based simulators. Their inherent differentiability and speed make them particularly well-suited for inverse design problems. Yet, adapting to new tasks from limited available data is an important aspect for real-world applications that current methods struggle with. We frame mesh-based simulation as a meta-learning problem and use a recent Bayesian meta-learning method to improve GNSs adaptability to new scenarios by leveraging context data and handling uncertainties. Our approach, latent task-specific graph network simulator, uses non-amortized task posterior approximations to sample latent descriptions of unknown system properties. Additionally, we leverage movement primitives for efficient full trajectory prediction, effectively addressing the issue of accumulating errors encountered by previous auto-regressive methods. We validate the effectiveness of our approach through various experiments, performing on par with or better than established baseline methods. Movement primitives further allow us to accommodate various types of context data, as demonstrated through the utilization of point clouds during inference. By combining GNSs with meta-learning, we bring them closer to real-world applicability, particularly in scenarios with smaller datasets.

翻译：模拟动态物理交互是多个科学领域的核心挑战，其应用涵盖机器人技术到材料科学。对于基于网格的模拟，图网络模拟器（GNS）为传统基于物理的模拟器提供了高效替代方案。其固有的可微分性和速度使其特别适用于逆向设计问题。然而，当前方法在处理有限可用数据的新任务适应能力方面仍显不足。我们将基于网格的模拟构建为元学习问题，并采用近期提出的贝叶斯元学习方法，通过利用上下文数据和处理不确定性来提升GNS对新场景的适应性。我们的方法——潜在任务特定图网络模拟器——采用非摊销任务后验近似，对未知系统属性的潜在描述进行采样。此外，我们利用运动基元进行高效的全轨迹预测，有效解决了先前自回归方法遇到的累积误差问题。通过多项实验验证了该方法的有效性，其性能与既定基线方法相当或更优。运动基元还使我们能够容纳多种类型的上下文数据，这通过在推理过程中利用点云数据得到体现。通过将GNS与元学习相结合，我们将其推向更接近真实世界应用，特别是在小数据集场景中。