428 citations · 1.5k across the 19 of their papers we have counts for
19 papers
Soft matter physics of the ground beneath our feet
Anne Voigtländer, Morgane Houssais, Karol A. Bacik +24
Inspired by presentations by the authors during a workshop organized at the Princeton Center for Theoretical Science (PCTS) in January 2022, we present a perspective on some of the…
A Darcy-Cahn-Hilliard model of multiphase fluid-driven fracture
Alexandre Guével, Yue Meng, Christian Peco +2
A Darcy-Cahn-Hilliard model coupled with damage is developed to describe multiphase-flow and fluid-driven fracturing in porous media. The model is motivated by recent experimental…
Crossover from viscous fingering to fracturing in cohesive wet granular media: a photoporomechanics study
Yue Meng, Wei Li, Ruben Juanes
We study fluid-induced deformation and fracture of cohesive granular media, and apply photoporomechanics to uncover the underpinning grain-scale mechanics. We fabricate photoelasti…
The FluidFlower International Benchmark Study: Process, Modeling Results, and Comparison to Experimental Data
Bernd Flemisch, Jan M. Nordbotten, Martin Fernø +30
Successful deployment of geological carbon storage (GCS) requires an extensive use of reservoir simulators for screening, ranking and optimization of storage sites. However, the ti…
Characteristics of fluid-fluid displacement in model mixed-wet porous media: patterns, pressures, and scalings
Ashkan Irannezhad, Bauyrzhan K. Primkulov, Ruben Juanes +1
We study the characteristics of fluid-fluid displacement in simple mixed-wet porous micromodels numerically using a dynamic pore network model. The porous micromodel consists of di…
Direct comparison of numerical simulations and experiments of CO injection and migration in geologic media: Value of local data and predictability
Lluís Saló-Salgado, Malin Haugen, Kristoffer Eikehaug +3
The accuracy and robustness of numerical models of geologic CO sequestration are almost never quantified with respect to direct observations that provide a ground truth. Here,…
Physical variability in meter-scale laboratory CO injections in faulted geometries
Malin Haugen, Lluis Salo-Salgado, Kristoffer Eikehaug +7
Carbon, capture, and storage (CCS) is an important bridging technology to combat climate change in the transition towards net-zero. The FluidFlower concept has been developed to vi…
Inverse modeling of nonisothermal multiphase poromechanics using physics-informed neural networks
Danial Amini, Ehsan Haghighat, Ruben Juanes
We propose a solution strategy for parameter identification in multiphase thermo-hydro-mechanical (THM) processes in porous media using physics-informed neural networks (PINNs). We…
Fluid-fluid displacement in mixed-wet porous media
Ashkan Irannezhad, Bauyrzhan K. Primkulov, Ruben Juanes +1
It is well-known that wettability exerts fundamental control over multiphase flow in porous media, which has been extensively studied in uniform-wet porous media. In contrast, mult…
Physics-informed neural network solution of thermo-hydro-mechanical (THM) processes in porous media
Danial Amini, Ehsan Haghighat, Ruben Juanes
Physics-Informed Neural Networks (PINNs) have received increased interest for forward, inverse, and surrogate modeling of problems described by partial differential equations (PDE)…
Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential training
Ehsan Haghighat, Danial Amini, Ruben Juanes
Physics-informed neural networks (PINNs) have received significant attention as a unified framework for forward, inverse, and surrogate modeling of problems governed by partial dif…
Deep learning for solution and inversion of structural mechanics and vibrations
Ehsan Haghighat, Ali Can Bekar, Erdogan Madenci +1
Deep learning has been the most popular machine learning method in the last few years. In this chapter, we present the application of deep learning and physics-informed neural netw…
A nonlocal physics-informed deep learning framework using the peridynamic differential operator
Ehsan Haghighat, Ali Can Bekar, Erdogan Madenci +1
The Physics-Informed Neural Network (PINN) framework introduced recently incorporates physics into deep learning, and offers a promising avenue for the solution of partial differen…
SciANN: A Keras/Tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks
Ehsan Haghighat, Ruben Juanes
In this paper, we introduce SciANN, a Python package for scientific computing and physics-informed deep learning using artificial neural networks. SciANN uses the widely used deep-…
A deep learning framework for solution and discovery in solid mechanics
Ehsan Haghighat, Maziar Raissi, Adrian Moure +2
We present the application of a class of deep learning, known as Physics Informed Neural Networks (PINN), to learning and discovery in solid mechanics. We explain how to incorporat…
Signatures of fluid-fluid displacement in porous media: wettability, patterns, and pressures
Bauyrzhan K. Primkulov, Amir A. Pahlavan, Xiaojing Fu +3
We develop a novel `moving capacitor' dynamic network model to simulate immiscible fluid-fluid displacement in porous media. Traditional network models approximate the pore geometr…
Anomalous transport in disordered fracture networks: spatial Markov model for dispersion with variable injection modes
Peter Kang, Marco Dentz, Tanguy Le Borgne +2
We investigate tracer transport on random discrete fracture networks that are characterized by the statistics of the fracture geometry and hydraulic conductivity. While it is well…
Self-organization of network dynamics into local quantized states
Christos Nicolaides, Ruben Juanes, Luis Cueto-Felgueroso
Self-organization and pattern formation in network-organized systems emerges from the collective activation and interaction of many interconnected units. A striking feature of thes…
Fluid Mixing from Viscous Fingering
Birendra Jha, Luis Cueto-Felgueroso, Ruben Juanes
Viscous fingering is a well-known hydrodynamic instability that sets in when a less viscous fluid displaces a more viscous fluid. When the two fluids are miscible, viscous fingerin…