论文标题
学习同质系统的模块化模拟
Learning Modular Simulations for Homogeneous Systems
论文作者
论文摘要
复杂的系统通常被分解为模块化子系统，以进行工程障碍。尽管各种基于方程式的白盒建模技术利用了这种结构，但基于学习的方法尚未广泛纳入这些想法。我们提出了一个模块化模拟框架，用于建模均匀的多体动力学系统，该系统结合了图神经网络和神经微分方程的想法。我们学会将单个动态子系统作为神经颂模模块建模。复合系统的完整模拟是通过在这些模块之间传递的时空消息来精心策划的。可以将任意数量的模块组合在一起，以模拟各种耦合拓扑的系统。我们在各种系统上评估了我们的框架，并表明消息传递允许随着时间的推移在多个模块之间进行协调，以进行准确的预测，在某些情况下，将零弹性概括性启用到新的系统配置。此外，我们证明，与从头开始培训的模型相比，我们的模型可以转移到具有较低数据要求和培训工作的新系统配置中。
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.
