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.

利用最近的贝叶斯元学习方法将网格模拟作为元学习问题来处理，通过利用上下文数据和处理不确定性来提高图网络模拟器对新场景的适应性，结合运动原语实现高效的全轨迹预测，并验证了该方法的有效性。

潜在的任务特定图网络模拟器

Latent Task-Specific Graph Network Simulators

In this work we consider data-driven optimization problems where one must
maximize a function given only queries at a fixed set of points. This problem
setting emerges in many domains where function evaluation is a complex and
expensive process, such as in the design of materials, vehicles, or neural
network architectures. Because the available data typically only covers a small
manifold of the possible space of inputs, a principal challenge is to be able
to construct algorithms that can reason about uncertainty and
out-of-distribution values, since a naive optimizer can easily exploit an
estimated model to return adversarial inputs. We propose to tackle this problem
by leveraging the normalized maximum-likelihood (NML) estimator, which provides
a principled approach to handling uncertainty and out-of-distribution inputs.
While in the standard formulation NML is intractable, we propose a tractable
approximation that allows us to scale our method to high-capacity neural
network models. We demonstrate that our method can effectively optimize
high-dimensional design problems in a variety of disciplines such as chemistry,
biology, and materials engineering.

本文提出了在数据驱动的优化问题中利用归一化极大似然估计器解决不确定性和变化范围问题的方法，并通过在各个领域（如化学、生物学和材料工程）的高维设计问题中进行的实验验证了该方法的有效性。