AutoKE: An automatic knowledge embedding framework for scientific machine learning. (arXiv:2205.05390v1 [cs.LG])

Imposing physical constraints on neural networks as a method of knowledge
embedding has achieved great progress in solving physical problems described by
governing equations. However, for many engineering problems, governing
equations often have complex forms, including complex partial derivatives or
stochastic physical fields, which results in significant inconveniences from
the perspective of implementation. In this paper, a scientific machine learning
framework, called AutoKE, is proposed, and a reservoir flow problem is taken as
an instance to demonstrate that this framework …

arxiv embedding framework knowledge learning machine machine learning