Application of deep learning reduced-order modeling for single-phase flow in faulted porous media

Artificial neural networks and deep learning (68T07) Flows in porous media; filtration; seepage (76S05) Finite element methods applied to problems in fluid mechanics (76M10)

Cites Work

Title not available (Why is that?)
Title not available (Why is that?)
A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs
Differential evolution -- a simple and efficient heuristic for global optimization over continuous spaces
Dynamic data-driven reduced-order models
Optimal nonlinear approximation
An introduction to multipoint flux approximations for quadrilateral grids
POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition
Surrogate modeling of elasto-plastic problems via long short-term memory neural networks and proper orthogonal decomposition
Unified approach to discretization of flow in fractured porous media
Dimension reduction of large-scale systems. Proceedings of a workshop, Oberwolfach, Germany, October 19--25, 2003.
A general multipurpose interpolation procedure: The magic points
The approximation of one matrix by another of lower rank.
Physics-data combined machine learning for parametric reduced-order modelling of nonlinear dynamical systems in small-data regimes
A survey of projection-based model reduction methods for parametric dynamical systems
A mixed finite element method for Darcy flow in fractured porous media with non-matching grids
A reduced model for Darcy’s problem in networks of fractures
Certified Reduced Basis Methods for Parametrized Partial Differential Equations
Dynamic mode decomposition of numerical and experimental data
Robust Discretization of Flow in Fractured Porous Media
Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization
Data-Driven Science and Engineering
Sensitivity Analysis in Practice
Mixed Finite Element Methods and Applications
Polyhedral Methods in Geosciences
A deep learning approach to Reduced Order Modelling of parameter dependent partial differential equations
Efficient geometrical parametrisation techniques of interfaces for reduced-order modelling: application to fluid–structure interaction coupling problems
Nonlinear mode decomposition with convolutional neural networks for fluid dynamics
Modeling Fractures and Barriers as Interfaces for Flow in Porous Media
Reduced Basis Methods for Partial Differential Equations
A non‐linear non‐intrusive reduced order model of fluid flow by auto‐encoder and self‐attention deep learning methods
Approximation bounds for convolutional neural networks in operator learning
Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based reduced order models

This page was built for publication: Application of deep learning reduced-order modeling for single-phase flow in faulted porous media

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6662482)