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 …
U-no: U-shaped neural operators
Neural operators generalize classical neural networks to maps between infinite-dimensional
spaces, eg, function spaces. Prior works on neural operators proposed a series of novel …
spaces, eg, function spaces. Prior works on neural operators proposed a series of novel …
Review of physics-informed machine-learning inversion of geophysical data
GT Schuster, Y Chen, S Feng - Geophysics, 2024 - library.seg.org
We review five types of physics-informed machine-learning (PIML) algorithms for inversion
and modeling of geophysical data. Such algorithms use the combination of a data-driven …
and modeling of geophysical data. Such algorithms use the combination of a data-driven …
3D elastic wave propagation with a factorized Fourier neural operator (F-FNO)
Numerical simulations are computationally demanding in three-dimensional (3D) settings
but they are often required to accurately represent physical phenomena. Neural operators …
but they are often required to accurately represent physical phenomena. Neural operators …
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 …
Lemon: Learning to learn multi-operator networks
Single-operator learning involves training a deep neural network to learn a specific operator,
whereas recent work in multi-operator learning uses an operator embedding structure to …
whereas recent work in multi-operator learning uses an operator embedding structure to …
Out-of-distributional risk bounds for neural operators with applications to the Helmholtz equation
Despite their remarkable success in approximating a wide range of operators defined by
PDEs, existing neural operators (NOs) do not necessarily perform well for all physics …
PDEs, existing neural operators (NOs) do not necessarily perform well for all physics …
Predictions of transient vector solution fields with sequential deep operator network
The deep operator network (DeepONet) structure has shown great potential in
approximating complex solution operators with low generalization errors. Recently, a …
approximating complex solution operators with low generalization errors. Recently, a …
HOSSNet: An efficient physics-guided neural network for simulating micro-crack propagation
Abstract The Hybrid Optimization Software Suite (HOSS), which combines the finite-discrete
element method (FDEM), is an advanced approach for simulating high-fidelity fracture and …
element method (FDEM), is an advanced approach for simulating high-fidelity fracture and …
Transfer learning Fourier neural operator for solving parametric frequency-domain wave equations
Fourier neural operator (FNO) is a recently proposed data-driven scheme to approximate the
implicit operators characterized by partial differential equations (PDEs) between functional …
implicit operators characterized by partial differential equations (PDEs) between functional …