论文标题
从不规则采样的部分观察中学习连续的系统动力学
Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations
论文作者
论文摘要
许多现实世界的系统,例如移动行星,都可以视为多代理动态系统,在该系统中,对象相互交互并与时间共同发展。这种动态通常很难捕获,并且基于观察到的对象轨迹的动态理解和预测动态成为许多领域的关键研究问题。但是,大多数现有的算法都假定观察值定期采样,并且在每个采样时间都可以完全观察到所有对象,这对于许多应用来说是不切实际的。在本文中,我们建议从不规则采样的部分观测值中学习系统动力学,并首次具有基础图结构。为了应对上述挑战,我们提出了LG-ODE,这是一种潜在的普通微分方程生成模型,用于建模具有已知图形结构的多代理动态系统。它可以同时学习高维轨迹的嵌入并推断连续的潜在系统动力学。我们的模型采用图形神经网络参数化的新颖编码器,可以从不规则地采样的结构对象的部分观察中推断出初始状态,并利用神经模型推断任意复杂的连续时间潜在的潜在潜在动力学。运动捕获,弹簧系统和带电粒子数据集的实验证明了我们方法的有效性。
Many real-world systems, such as moving planets, can be considered as multi-agent dynamic systems, where objects interact with each other and co-evolve along with the time. Such dynamics is usually difficult to capture, and understanding and predicting the dynamics based on observed trajectories of objects become a critical research problem in many domains. Most existing algorithms, however, assume the observations are regularly sampled and all the objects can be fully observed at each sampling time, which is impractical for many applications. In this paper, we propose to learn system dynamics from irregularly-sampled partial observations with underlying graph structure for the first time. To tackle the above challenge, we present LG-ODE, a latent ordinary differential equation generative model for modeling multi-agent dynamic system with known graph structure. It can simultaneously learn the embedding of high dimensional trajectories and infer continuous latent system dynamics. Our model employs a novel encoder parameterized by a graph neural network that can infer initial states in an unsupervised way from irregularly-sampled partial observations of structural objects and utilizes neuralODE to infer arbitrarily complex continuous-time latent dynamics. Experiments on motion capture, spring system, and charged particle datasets demonstrate the effectiveness of our approach.