428 citations · 1.6k across the 21 of their papers we have counts for
12 papers · 1 filter
STONet: A neural operator for modeling solute transport in micro-cracked reservoirs
Ehsan Haghighat, Mohammad Hesan Adeli, S Mohammad Mousavi +1
In this work, we introduce a novel neural operator, the Solute Transport Operator Network (STONet), to efficiently model contaminant transport in micro-cracked porous media. STONet…
An enrichment approach for enhancing the expressivity of neural operators with applications to seismology
Ehsan Haghighat, Umair bin Waheed, George Karniadakis
The Eikonal equation plays a central role in seismic wave propagation and hypocenter localization, a crucial aspect of efficient earthquake early warning systems. Despite recent pr…
Multiphysics discovery with moving boundaries using Ensemble SINDy and Peridynamic Differential Operator
A. C. Bekar, E. Haghighat, E. Madenci
This study proposes a novel framework for learning the underlying physics of phenomena with moving boundaries. The proposed approach combines Ensemble SINDy and Peridynamic Differe…
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…
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)…
Constitutive model characterization and discovery using physics-informed deep learning
Ehsan Haghighat, Sahar Abouali, Reza Vaziri
Classically, the mechanical response of materials is described through constitutive models, often in the form of constrained ordinary differential equations. These models have a ve…
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…
A Physics Informed Neural Network Approach to Solution and Identification of Biharmonic Equations of Elasticity
Mohammad Vahab, Ehsan Haghighat, Maryam Khaleghi +1
We explore an application of the Physics Informed Neural Networks (PINNs) in conjunction with Airy stress functions and Fourier series to find optimal solutions to a few reference…
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…
Physics-Informed Neural Network for Modelling the Thermochemical Curing Process of Composite-Tool Systems During Manufacture
Sina Amini Niaki, Ehsan Haghighat, Trevor Campbell +2
We present a Physics-Informed Neural Network (PINN) to simulate the thermochemical evolution of a composite material on a tool undergoing cure in an autoclave. In particular, we so…
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…
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…