  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.
