Complex systems are often decomposed into modular subsystems for engineering tractability. Although various equation based white-box modeling techniques make use of such structure, learning based methods have yet to incorporate these ideas broadly. We present a modular simulation framework for modeling homogeneous multibody dynamical systems, which combines ideas from graph neural networks and neural differential equations. We learn to model the individual dynamical subsystem as a neural ODE module. Full simulation of the composite system is orchestrated via spatio-temporal message passing between these modules. An arbitrary number of modules can be combined to simulate systems of a wide variety of coupling topologies. We evaluate our framework on a variety of systems and show that message passing allows coordination between multiple modules over time for accurate predictions and in certain cases, enables zero-shot generalization to new system configurations. Furthermore, we show that our models can be transferred to new system configurations with lower data requirement and training effort, compared to those trained from scratch.

翻译：复杂的系统往往被分解成模块化子系统,以便进行工程引力。虽然各种基于方程式的白箱建模技术利用了这种结构,但学习方法尚未广泛纳入这些想法。我们为模拟单一多体动态系统提供了一个模块化模拟框架,将图形神经网络和神经差异方程式中的想法结合起来。我们学会了将单个动态子系统建模成神经DE模块。合成系统的全面模拟是通过这些模块之间传递的时空信息来操作的。任意数量的模块可以与各种混合表层的模拟系统合并。我们评估了各种系统的框架,并表明信息传递可以使多个模块在一段时间内进行协调,以便准确预测,在某些情况下,使零光化的通用化成为新系统配置。此外,我们显示,与从零开始培训的模块相比,我们的模型可以转移到数据要求和培训努力较少的新系统配置。