Physics-Aware Deep-Learning-Based Proxy Reservoir Simulation Model Equipped With State and Well Output Prediction
Data-driven methods have been revolutionizing the way physicists and engineers handle complex and challenging problems even when the physics is not fully understood. However, these models very often lack interpretability. Physics-aware machine learning (ML) techniques have been used to endow proxy m...
Main Authors: | Emilio Jose Rocha Coutinho, Marcelo Dall’Aqua, Eduardo Gildin |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2021-09-01
|
Series: | Frontiers in Applied Mathematics and Statistics |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fams.2021.651178/full |
Similar Items
-
REDUCED-ORDER MODELLING OF PARAMETERIZED TRANSIENT FLOWS IN CLOSED-LOOP SYSTEMS
by: German Péter, et al.
Published: (2021-01-01) -
Reduced-order modeling for stochastic large-scale and time-dependent flow problems using deep spatial and temporal convolutional autoencoders
by: Azzedine Abdedou, et al.
Published: (2023-05-01) -
Uncertainty quantification for industrial numerical simulation using dictionaries of reduced order models
by: Daniel Thomas, et al.
Published: (2022-01-01) -
VpROM: a novel variational autoencoder-boosted reduced order model for the treatment of parametric dependencies in nonlinear systems
by: Thomas Simpson, et al.
Published: (2024-03-01) -
Static Aeroelasticity Using High Fidelity Aerodynamics in a Staggered Coupled and ROM Scheme
by: Angelos Kafkas, et al.
Published: (2020-11-01)