Web: http://arxiv.org/abs/2205.05390

May 12, 2022, 1:11 a.m. | Mengge Du, Yuntian Chen, Dongxiao Zhang

cs.CL updates on arXiv.org arxiv.org

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

More from arxiv.org / cs.CL updates on arXiv.org

Director, Applied Mathematics & Computational Research Division

@ Lawrence Berkeley National Lab | Berkeley, Ca

Business Data Analyst

@ MainStreet Family Care | Birmingham, AL

Assistant/Associate Professor of the Practice in Business Analytics

@ Georgetown University McDonough School of Business | Washington DC

Senior Data Science Writer

@ NannyML | Remote

Director of AI/ML Engineering

@ Armis Industries | Remote (US only), St. Louis, California

Digital Analytics Manager

@ Patagonia | Ventura, California