Real time accurate solutions of large scale complex dynamical systems are in critical need for control, optimization, uncertainty quantification, and decision-making in practical engineering and science applications. This paper contributes in this direction a model constrained tangent manifold learning (mcTangent) approach. At the heart of mcTangent is the synergy of several desirable strategies: i) a tangent manifold learning to take advantage of the neural network speed and the time accurate nature of the method of lines; ii) a model constrained approach to encode the neural network tangent with the underlying governing equations; iii) sequential learning strategies to promote long time stability and accuracy; and iv) data randomization approach to implicitly enforce the smoothness of the neural network tangent and its likeliness to the truth tangent up second order derivatives in order to further enhance the stability and accuracy of mcTangent solutions. Both semi heuristic and rigorous arguments are provided to analyze and justify the proposed approach. Several numerical results for transport equation, viscous Burgers equation, and Navier Stokes equation are presented to study and demonstrate the capability of the proposed mcTangent learning approach.
翻译:大规模复杂动态系统的实时准确解决方案对于实际工程和科学应用方面的控制、优化、不确定性量化和决策至关重要。本文件在这方面提供了一种模型,抑制了相切的多式学习(mcTangent)方法。McTangent的核心是若干可取战略的协同作用:(一) 利用神经网络速度和线方法的时间准确性进行分流的多重学习;(二) 将神经网络与基本治理方程式相切的模型限制方法;(三) 促进长期时间稳定性和准确性的连续学习战略;以及(四) 数据随机化方法,以暗示加强神经网络的顺畅性及其类似性,以进一步加强线性线方法的稳定性和准确性。为分析和论证拟议方法提供了半偏重和严格的论据。为运输方程式、布尔格斯方程式和纳维耶·斯托克斯方程式提供了若干数字结果,用于研究和展示拟议的McTangent学习方法的能力。