Simulation-based methods for statistical inference have evolved dramatically over the past
50 years, keeping pace with technological advancements. The field is undergoing a new …

Modern Bayesian inference involves a mixture of computational techniques for estimating,
validating, and drawing conclusions from probabilistic models as part of principled …

Since their initial introduction, score-based diffusion models (SDMs) have been successfully
applied to solve a variety of linear inverse problems in finite-dimensional vector spaces due …

We introduce a probabilistic technique for full-waveform inversion, using variational
inference and conditional normalizing flows to quantify uncertainty in migration-velocity …

We present a systematic study of conditional triangular transport maps in function spaces
from the perspective of optimal transportation and with a view towards amortized Bayesian …

We present a novel approach to transcranial ultrasound computed tomography that utilizes
normalizing flows to improve the speed of imaging and provide Bayesian uncertainty …

Solving multiphysics-based inverse problems for geological carbon storage monitoring can
be challenging when multimodal time-lapse data are expensive to collect and costly to …

We present a semi-amortized variational inference framework designed for computationally
feasible uncertainty quantification in full-waveform inversion to explore the multimodal …

Geoscientists use observed data to estimate properties of the Earth's interior. This often
requires non‐linear inverse problems to be solved and uncertainties to be estimated …

Most deep learning models for computational imaging regress a single reconstructed image.
In practice, however, ill-posedness, nonlinearity, model mismatch, and noise often conspire …