使用Transformers学习随机动力学和预测紧急行为
Learning stochastic dynamics and predicting emergent behavior using transformers
摘要：
我们表明，最初设计用于语言处理的神经网络可以通过观察系统的单个动力学轨迹来学习随机系统的动力学规则，并且可以在训练过程中未观察到的条件下准确预测其紧急行为。我们考虑一个晶格模型的活性物质进行连续时间蒙特卡罗动力学，模拟密度在其稳定状态包括小，分散的集群。我们在模型的单一轨迹上训练一个称为Transformer的神经网络。的Transformer，我们表明有能力代表的动态规则，是众多的和非本地的，学习这个模型的动态组成的一个小数目的进程。前向传播的轨迹的训练Transformer，在训练过程中没有遇到的密度，表现出运动诱导的相分离，因此预测的非平衡相变的存在。Transformers具有从观察中学习动力学规则的灵活性，而无需显式枚举速率或粗粒度的配置空间，因此这里使用的过程可以应用于广泛的物理系统，包括具有大型和复杂动态生成器的系统。
英文摘要：
We show that a neural network originally designed for language processing can learn the dynamical rules of a stochastic system by observation of a single dynamical trajectory of the system, and can accurately predict its emergent behavior under conditions not observed during training. We consider a lattice model of active matter undergoing continuous-time Monte Carlo dynamics, simulated at a density at which its steady state comprises small, dispersed clusters. We train a neural network called a transformer on a single trajectory of the model. The transformer, which we show has the capacity to represent dynamical rules that are numerous and nonlocal, learns that the dynamics of this model consists of a small number of processes. Forward-propagated trajectories of the trained transformer, at densities not encountered during training, exhibit motility-induced phase separation and so predict the existence of a nonequilibrium phase transition. Transformers have the flexibility to learn dynamical rules from observation without explicit enumeration of rates or coarse-graining of configuration space, and so the procedure used here can be applied to a wide range of physical systems, including those with large and complex dynamical generators.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
