Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations
Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations
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发表时间：
2020-11
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通讯作者：
Zijie Huang;Yizhou Sun;Wei Wang-
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作者：
Zijie Huang;Yizhou Sun;Wei Wang-
许多现实世界的系统，如移动的行星，可以被视为多智能体动态系统，其中对象相互作用并随着时间共同进化。这种动态通常难以捕捉，基于观察到的物体轨迹来理解和预测动态成为许多领域的关键研究问题。然而，大多数现有算法都假设观测值是定期采样的，并且在每次采样时都可以完全观察到所有的目标，这对于许多应用来说是不切实际的。本文首次提出了从具有底层图结构的不规则采样部分观测数据中学习系统动力学的方法。为了解决上述挑战，我们提出了LG-ODE，一种用于建模具有已知图结构的多智能体动态系统的潜在常微分方程生成模型。它可以同时学习高维轨迹的嵌入和推断连续的潜在系统动力学。我们的模型采用了一种由图神经网络参数化的新型编码器，该编码器可以从结构对象的不规则采样部分观测中以无监督的方式推断初始状态，并利用neuralODE推断任意复杂的连续时间潜在动力学。在运动捕捉、弹簧系统和带电粒子数据集上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.