论文标题
弥合差距：机器学习以解决不当建模的动态
Bridging the Gap: Machine Learning to Resolve Improperly Modeled Dynamics
论文作者
论文摘要
我们提出了一种数据驱动的建模策略，以克服表现出复杂时空行为的系统的不当建模动力学。我们提出了一个深度学习框架，以解决系统的真实动力学与系统模型所给出的动力学之间的差异，该模型是不准确或不充分描述的。我们的机器学习策略利用从实际系统的不当系统模型和观察数据生成的数据来创建神经网络，以建模实际系统的动态。我们使用从三个日益复杂的动力学系统获得的数值解决方案评估了提出的框架。我们的结果表明，我们的系统能够学习一个数据驱动的模型，该模型在以前未观察到的区域以及未来的状态中提供了对系统状态的准确估计。我们的结果表明，最先进的机器学习框架在估计系统真实动态的准确先验方面的力量可以用于预测到有限的地平线。
We present a data-driven modeling strategy to overcome improperly modeled dynamics for systems exhibiting complex spatio-temporal behaviors. We propose a Deep Learning framework to resolve the differences between the true dynamics of the system and the dynamics given by a model of the system that is either inaccurately or inadequately described. Our machine learning strategy leverages data generated from the improper system model and observational data from the actual system to create a neural network to model the dynamics of the actual system. We evaluate the proposed framework using numerical solutions obtained from three increasingly complex dynamical systems. Our results show that our system is capable of learning a data-driven model that provides accurate estimates of the system states both in previously unobserved regions as well as for future states. Our results show the power of state-of-the-art machine learning frameworks in estimating an accurate prior of the system's true dynamics that can be used for prediction up to a finite horizon.
