39 citations · 95 across the 10 of their papers we have counts for
10 papers
Bayesian inverse Navier-Stokes problems: joint flow field reconstruction and parameter learning
Alexandros Kontogiannis, Scott V. Elgersma, Andrew J. Sederman +1
We formulate and solve a Bayesian inverse Navier-Stokes (N-S) problem that assimilates velocimetry data in order to jointly reconstruct a 3D flow field and learn the unknown N-S pa…
Sub-sampling of NMR Correlation and Exchange Experiments
Julian B. B. Beckmann, Mick D. Mantle, Andrew J. Sederman +1
Sub-sampling is applied to simulated - NMR signals and its influence on inversion performance is evaluated. For this different levels of sub-sampling were employed ranging…
Deep Learning as a Method for Inversion of NMR Signals
Julian B. B. Beckmann, Mick D. Mantle, Andrew J. Sederman +1
The concept of deep learning is employed for the inversion of NMR signals and it is shown that NMR signal inversion can be considered as an image-to-image regression problem, which…
Generalized Cross-Validation as a Method of Hyperparameter Search for MTGV Regularization
Julian B. B. Beckmann, Amy Sparks, Jordan A. Ward-Williams +3
The concept of generalized cross-validation (GCV) is applied to modified total generalized variation (MTGV) regularization. Current implementations of the MTGV regularization rely…
Joint reconstruction and segmentation of noisy velocity images as an inverse Navier-Stokes problem
Alexandros Kontogiannis, Scott V. Elgersma, Andrew J. Sederman +1
We formulate and solve a generalized inverse Navier-Stokes problem for the joint velocity field reconstruction and boundary segmentation of noisy flow velocity images. To regulariz…
Joint phase reconstruction and magnitude segmentation from velocity-encoded MRI data
Veronica Corona, Martin Benning, Lynn F. Gladden +3
Velocity-encoded MRI is an imaging technique used in different areas to assess flow motion. Some applications include medical imaging such as cardiovascular blood flow studies, and…