72 citations · 99 across the 9 of their papers we have counts for
3 papers · 1 filter
Transfer learning-based physics-informed convolutional neural network for simulating flow in porous media with time-varying controls
Jungang Chen, Eduardo Gildin, John E. Killough
A physics-informed convolutional neural network is proposed to simulate two phase flow in porous media with time-varying well controls. While most of PICNNs in existing literatures…
Physics-informed Convolutional Recurrent Surrogate Model for Reservoir Simulation with Well Controls
Jungang Chen, Eduardo Gildin, John E. Killough
This paper presents a novel surrogate model for modeling subsurface fluid flow with well controls using a physics-informed convolutional recurrent neural network (PICRNN). The mode…
Investigation of Proper Orthogonal Decomposition for Echo State Networks
Jean Panaioti Jordanou, Eric Aislan Antonelo, Eduardo Camponogara +1
Echo State Networks (ESN) are a type of Recurrent Neural Network that yields promising results in representing time series and nonlinear dynamic systems. Although they are equipped…