A deep learning based reduced order modeling for stochastic underground flow problems
From MaRDI portal
Publication:2162031
DOI10.1016/j.jcp.2022.111449OpenAlexW3215686539MaRDI QIDQ2162031
Shubin Fu, Eric T. Chung, Yiran Wang
Publication date: 5 August 2022
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2111.13372
Basic methods in fluid mechanics (76Mxx) Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65Mxx) Numerical methods for partial differential equations, boundary value problems (65Nxx)
Related Items
A locally conservative multiscale method for stochastic highly heterogeneous flow, Partial learning using partially explicit discretization for multicontinuum/multiscale problems with limited observation: language interactions simulation
Uses Software
Cites Work
- Iterative Galerkin-enriched multiscale finite-volume method
- Comparison between reduced basis and stochastic collocation methods for elliptic problems
- A localized approach for the method of approximate particular solutions
- Adaptive multiscale model reduction with generalized multiscale finite element methods
- Reduced basis approximation and a posteriori error estimation for affinely parametrized elliptic coercive partial differential equations. Application to transport and continuum mechanics.
- The variational multiscale method -- a paradigm for computational mechanics
- A multiscale finite element method for elliptic problems in composite materials and porous media
- Analysis of upscaling absolute permeability
- Non-intrusive reduced order modeling of nonlinear problems using neural networks
- An `empirical interpolation' method: Application to efficient reduced-basis discretization of partial differential equations
- Data-driven reduced order modeling for time-dependent problems
- An improved localized method of approximate particular solutions for solving elliptic PDEs
- A local-global multiscale method for highly heterogeneous stochastic groundwater flow problems
- A localized reduced-order modeling approach for PDEs with bifurcating solutions
- Accelerating flash calculation through deep learning methods
- Solving many-electron Schrödinger equation using deep neural networks
- Less is often more: applied inverse problems using \(hp\)-forward models
- A self-adaptive deep learning algorithm for accelerating multi-component flash calculation
- Multiscale modeling of heat and mass transfer in fractured media for enhanced geothermal systems applications
- Constrained energy minimization based upscaling for coupled flow and mechanics
- Residual-driven online generalized multiscale finite element methods
- Efficient model reduction of parametrized systems by matrix discrete empirical interpolation
- A multiscale mortar multipoint flux mixed finite element method
- PROPER ORTHOGONAL DECOMPOSITION AND ITS APPLICATIONS—PART I: THEORY
- Certified Reduced Basis Methods for Parametrized Partial Differential Equations
- Weighted Poincaré Inequalities and Applications in Domain Decomposition
- Nonlinear Model Reduction via Discrete Empirical Interpolation
- Reduced-Contrast Approximations for High-Contrast Multiscale Flow Problems
- A Stochastic Mortar Mixed Finite Element Method for Flow in Porous Media with Multiple Rock Types
- Multiscale Finite Element Methods
- Solving Nonlinear Equations with Newton's Method
- A mixed multiscale finite element method for elliptic problems with oscillating coefficients
- On the Convergence Rates of GMsFEMs for Heterogeneous Elliptic Problems Without Oversampling Techniques
- A Multiscale Mortar Mixed Finite Element Method
- Reduced Basis Methods for Partial Differential Equations
- Application of the Karhunen-Loève Expansion to Feature Selection and Ordering
- A logical calculus of the ideas immanent in nervous activity
