A physics-informed and hierarchically regularized data-driven model for predicting fluid flow through porous media

DOI10.1016/j.jcp.2021.110526OpenAlexW3174177148WikidataQ115041756 ScholiaQ115041756MaRDI QIDQ2132604

Qinjun Kang, Kane C. Bennett, Nicholas Lubbers, Mohamed Mehana, Timothy C. Germann, Yu Chen, Hari S. Viswanathan, Kun Wang

Publication date: 28 April 2022

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jcp.2021.110526

zbMATH Keywords

molecular dynamicsCAEmachine learningshalelattice Boltzmannporous flow

Mathematics Subject Classification ID

Basic methods in fluid mechanics (76Mxx) Artificial intelligence (68Txx) Flows in porous media; filtration; seepage (76Sxx)

Related Items (2)

A surrogate model for the prediction of permeabilities and flow through porous media: a machine learning approach based on stochastic Brownian motion ⋮ Incremental neural controlled differential equations for modeling of path-dependent material behavior

Uses Software

• darch
• TensorFlow
• ImageNet
• U-Net
• PDE-Net
• AlexNet

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Stochastic generation of explicit pore structures by thresholding Gaussian random fields
• Multiphase lattice Boltzmann simulations for porous media applications. A review
• Numerical calculation of multiphase fluid flow
• Fast parallel algorithms for short-range molecular dynamics
• Machine learning materials physics: integrable deep neural networks enable scale bridging by learning free energy functions
• The neural particle method - an updated Lagrangian physics informed neural network for computational fluid dynamics
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Modeling groundwater flow and contaminant transport
• Data-driven computational mechanics
• A simplified MAC technique for incompressible fluid flow calculations
• Direct pore-level modeling of incompressible fluid flow in porous media
• Reducing the Dimensionality of Data with Neural Networks
• Numerical Calculation of Time-Dependent Viscous Incompressible Flow of Fluid with Free Surface
• Stabilized Finite Element Formulations for Incompressible Flow Computations
• Data-Driven Science and Engineering
• LATTICE BOLTZMANN METHOD FOR FLUID FLOWS
• Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
• On the Convergence of Discrete Approximations to the Navier-Stokes Equations
• Modelling two-phase flow in porous media at the pore scale using the volume-of-fluid method

This page was built for publication: A physics-informed and hierarchically regularized data-driven model for predicting fluid flow through porous media