Physics-Informed Machine Learning for Real-time Reservoir Management.
Maruti Kumar Mudunuru(Los Alamos National Laboratory), Hari Viswanathan(Los Alamos National Laboratory), Shriram Srinivasan(Los Alamos National Laboratory), Timothy R. Carr, Bill Carey(Los Alamos National Laboratory), Jeffrey D. Hyman(Los Alamos National Laboratory), Velimir V. Vesselinov(Los Alamos National Laboratory), Satish Karra(Los Alamos National Laboratory), N. Welch(Los Alamos National Laboratory), Qinjun Kang(Los Alamos National Laboratory), Luke Frash(Los Alamos National Laboratory), Matthew Sweeney(Los Alamos National Laboratory), Daniel O’Malley, Michael R. Gross(Los Alamos National Laboratory), Liwei Li(Northwest A&F University), Rajesh Pawar(Los Alamos National Laboratory), George Guthrie(Los Alamos National Laboratory), Hongwu Xu(Xi'an University of Architecture and Technology)
National Conference on Artificial Intelligence
January 1, 2020
Cited by 6
Related Papers
Hydrogen Adsorption in a Highly Stable Porous Rare-Earth Metal-Organic Framework: Sorption Properties and Neutron Diffraction Studies
|Journal of the American Chemical Society|2008|314
A New Molybdenum Nitride Catalyst with Rhombohedral MoS<sub>2</sub> Structure for Hydrogenation Applications
|Journal of the American Chemical Society|2015|231
Enhanced Structural Stability and Photo Responsiveness of CH<sub>3</sub>NH<sub>3</sub>SnI<sub>3</sub> Perovskite via Pressure‐Induced Amorphization and Recrystallization
|Advanced Materials|2016|229
Antiperovskite Li<sub>3</sub>OCl Superionic Conductor Films for Solid‐State Li‐Ion Batteries
|Advanced Science|2016|219
Storage and separation applications of nanoporous metal–organic frameworks
|CrystEngComm|2009|171