Self-consuming generative models go mad
Seismic advances in generative AI algorithms for imagery, text, and other data types has led
to the temptation to use synthetic data to train next-generation models. Repeating this …
to the temptation to use synthetic data to train next-generation models. Repeating this …
Fourier-DeepONet: Fourier-enhanced deep operator networks for full waveform inversion with improved accuracy, generalizability, and robustness
Full waveform inversion (FWI) infers the subsurface structure information from seismic
waveform data by solving a non-convex optimization problem. Data-driven FWI has been …
waveform data by solving a non-convex optimization problem. Data-driven FWI has been …
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 …
Representation in AI evaluations
Calls for representation in artificial intelligence (AI) and machine learning (ML) are
widespread, with" representation" or" representativeness" generally understood to be both …
widespread, with" representation" or" representativeness" generally understood to be both …
Neural inverse operators for solving PDE inverse problems
A large class of inverse problems for PDEs are only well-defined as mappings from
operators to functions. Existing operator learning frameworks map functions to functions and …
operators to functions. Existing operator learning frameworks map functions to functions and …
En-DeepONet: An enrichment approach for enhancing the expressivity of neural operators with applications to seismology
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 …
localization, a crucial aspect of efficient earthquake early warning systems. Despite recent …
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 …
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 …
WISE: Full-waveform variational inference via subsurface extensions
We introduce a probabilistic technique for full-waveform inversion, using variational
inference and conditional normalizing flows to quantify uncertainty in migration-velocity …
inference and conditional normalizing flows to quantify uncertainty in migration-velocity …
Turbulence in focus: Benchmarking scaling behavior of 3d volumetric super-resolution with blastnet 2.0 data
Abstract Analysis of compressible turbulent flows is essential for applications related to
propulsion, energy generation, and the environment. Here, we present BLASTNet 2.0, a 2.2 …
propulsion, energy generation, and the environment. Here, we present BLASTNet 2.0, a 2.2 …