The study of interacting dynamical systems continues to attract research
interest in various fields of science and engineering. In a collection of
interacting particles, the interaction network contains information about how
various components interact with one another. Inferring the information about
the interaction network from the dynamics of agents is a problem of
long-standing interest. In this work, we employ a self-supervised neural
network model to achieve two outcomes: to recover the interaction network and
to predict the dynamics of individual agents. Both these information are
inferred solely from the observed trajectory data. This work presents an
application of the Neural Relational Inference model to two dynamical systems:
coupled particles mediated by Hooke's law interaction and coupled phase
(Kuramoto) oscillators.

利用自监督神经网络模型从观察到的轨迹数据中恢复相互作用网络并预测个体动力学的研究，应用于耦合粒子和耦合振荡器（Kuramoto）的两个动力系统。

耦合动力系统中的相互作用网络的机器学习

Machine learning the interaction network in coupled dynamical systems

The creation of high-fidelity computer-generated (CG) characters used in film
and gaming requires intensive manual labor and a comprehensive set of facial
assets to be captured with complex hardware, resulting in high cost and long
production cycles. In order to simplify and accelerate this digitization
process, we propose a framework for the automatic generation of high-quality
dynamic facial assets, including rigs which can be readily deployed for artists
to polish. Our framework takes a single scan as input to generate a set of
personalized blendshapes, dynamic and physically-based textures, as well as
secondary facial components (e.g., teeth and eyeballs). Built upon a facial
database consisting of pore-level details, with over $4,000$ scans of varying
expressions and identities, we adopt a self-supervised neural network to learn
personalized blendshapes from a set of template expressions. We also model the
joint distribution between identities and expressions, enabling the inference
of the full set of personalized blendshapes with dynamic appearances from a
single neutral input scan. Our generated personalized face rig assets are
seamlessly compatible with cutting-edge industry pipelines for facial animation
and rendering. We demonstrate that our framework is robust and effective by
inferring on a wide range of novel subjects, and illustrate compelling
rendering results while animating faces with generated customized
physically-based dynamic textures.

通过使用自监督神经网络，本文提出了一个自动生成高质量动态面部组件的框架，包括可以快速部署供艺术家打磨的模型，并展示了在广泛的新主题上推断的鲁棒性和有效性，同时通过生成定制的基于物理的动态纹理来给角色面部制作更生动的动画。