Neural‐network‐based regularization methods for inverse problems in imaging
This review provides an introduction to—and overview of—the current state of the art in
neural‐network based regularization methods for inverse problems in imaging. It aims to …
neural‐network based regularization methods for inverse problems in imaging. It aims to …
Solution of physics-based Bayesian inverse problems with deep generative priors
Inverse problems are ubiquitous in nature, arising in almost all areas of science and
engineering ranging from geophysics and climate science to astrophysics and …
engineering ranging from geophysics and climate science to astrophysics and …
Trustworthy AI for human-centric smart manufacturing: A survey
Human-centric smart manufacturing (HCSM) envisions a symbiotic relationship between
humans and machines, leveraging human capability and Artificial Intelligence (AI)'s …
humans and machines, leveraging human capability and Artificial Intelligence (AI)'s …
Bayesian geophysical inversion using invertible neural networks
Constraining geophysical models with observed data usually involves solving nonlinear and
nonunique inverse problems. Neural mixture density networks (MDNs) provide an efficient …
nonunique inverse problems. Neural mixture density networks (MDNs) provide an efficient …
Reliable amortized variational inference with physics-based latent distribution correction
Bayesian inference for high-dimensional inverse problems is computationally costly and
requires selecting a suitable prior distribution. Amortized variational inference addresses …
requires selecting a suitable prior distribution. Amortized variational inference addresses …
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 …
Preconditioned training of normalizing flows for variational inference in inverse problems
Obtaining samples from the posterior distribution of inverse problems with expensive
forward operators is challenging especially when the unknowns involve the strongly …
forward operators is challenging especially when the unknowns involve the strongly …
The efficacy and generalizability of conditional GANs for posterior inference in physics-based inverse problems
In this work, we train conditional Wasserstein generative adversarial networks to effectively
sample from the posterior of physics-based Bayesian inference problems. The generator is …
sample from the posterior of physics-based Bayesian inference problems. The generator is …
Deep Bayesian inference for seismic imaging with tasks
We use techniques from Bayesian inference and deep neural networks to translate
uncertainty in seismic imaging to uncertainty in tasks performed on the image, such as …
uncertainty in seismic imaging to uncertainty in tasks performed on the image, such as …
Using convolutional neural networks to develop starting models for near-surface 2-D full waveform inversion
Non-invasive subsurface imaging using full waveform inversion (FWI) has the potential to
fundamentally change near-surface (< 30 m) site characterization by enabling the recovery …
fundamentally change near-surface (< 30 m) site characterization by enabling the recovery …