A critical review of physics-informed machine learning applications in subsurface energy systems
Abstract Machine learning has emerged as a powerful tool in various fields, including
computer vision, natural language processing, and speech recognition. It can unravel …
computer vision, natural language processing, and speech recognition. It can unravel …
Simulating seismic multifrequency wavefields with the Fourier feature physics-informed neural network
To simulate seismic wavefields with a frequency-domain wave equation, conventional
numerical methods must solve the equation sequentially to obtain the wavefields for different …
numerical methods must solve the equation sequentially to obtain the wavefields for different …
Self-adaptive physics-driven deep learning for seismic wave modeling in complex topography
Y Ding, S Chen, X Li, S Wang, S Luan, H Sun - Engineering Applications of …, 2023 - Elsevier
Solving for the scattered wavefield is a key scientific problem in the field of seismology and
earthquake engineering. Physics-informed neural networks (PINNs) developed in recent …
earthquake engineering. Physics-informed neural networks (PINNs) developed in recent …
Constitutive model characterization and discovery using physics-informed deep learning
E Haghighat, S Abouali, R Vaziri - Engineering Applications of Artificial …, 2023 - Elsevier
Constitutive models are fundamental blocks of modeling physical processes, where they
connect conservation laws with the kinematics of the system. They are often expressed in …
connect conservation laws with the kinematics of the system. They are often expressed in …
SeismicNet: Physics-informed neural networks for seismic wave modeling in semi-infinite domain
Recently, there has been an increasing interest in leveraging physics-informed neural
networks (PINNs) for modeling dynamical systems. However, limited studies have been …
networks (PINNs) for modeling dynamical systems. However, limited studies have been …
Seismic inversion based on acoustic wave equations using physics-informed neural network
Seismic inversion is a significant tool for exploring the structure and characteristics of the
underground. However, the conventional inversion strategy strongly depends on the initial …
underground. However, the conventional inversion strategy strongly depends on the initial …
Solving seismic wave equations on variable velocity models with Fourier neural operator
In the study of subsurface seismic imaging, solving the acoustic wave equation is a pivotal
component in existing models. The advancement of deep learning (DL) enables solving …
component in existing models. The advancement of deep learning (DL) enables solving …
Deep neural Helmholtz operators for 3D elastic wave propagation and inversion
Numerical simulations of seismic wave propagation in heterogeneous 3D media are central
to investigating subsurface structures and understanding earthquake processes, yet are …
to investigating subsurface structures and understanding earthquake processes, yet are …
Helmholtz-equation solution in nonsmooth media by a physics-informed neural network incorporating quadratic terms and a perfectly matching layer condition
Frequency-domain simulation of seismic waves plays an important role in seismic inversion,
but it remains challenging in large models. The recently proposed physics-informed neural …
but it remains challenging in large models. The recently proposed physics-informed neural …
A novel physics-informed neural network for modeling electromagnetism of a permanent magnet synchronous motor
This study presents a novel physics-informed neural network (PINN) architecture designed
to address the challenges of replicating an electric motor. The proposed architecture has …
to address the challenges of replicating an electric motor. The proposed architecture has …