29 citations · 82 across the 6 of their papers we have counts for
6 papers
An Analysis of Deep Learning Parameterizations for Ocean Subgrid Eddy Forcing
Cem Gultekin, Adam Subel, Cheng Zhang +5
Due to computational constraints, climate simulations cannot resolve a range of small-scale physical processes, which have a significant impact on the large-scale evolution of the…
Cramér-Rao Bound Optimized Subspace Reconstruction in Quantitative MRI
Andrew Mao, Sebastian Flassbeck, Cem Gultekin +1
We extend the traditional framework for estimating subspace bases that maximize the preserved signal energy to additionally preserve the Cramér-Rao bound (CRB) of the biophysical p…
Implementation and Evaluation of a Machine Learned Mesoscale Eddy Parameterization into a Numerical Ocean Circulation Model
Cheng Zhang, Pavel Perezhogin, Cem Gultekin +3
We address the question of how to use a machine learned parameterization in a general circulation model, and assess its performance both computationally and physically. We take one…
Rapid quantitative magnetization transfer imaging: utilizing the hybrid state and the generalized Bloch model
Jakob Assländer, Cem Gultekin, Andrew Mao +7
Purpose: To explore efficient encoding schemes for quantitative magnetization transfer (qMT) imaging with few constraints on model Theory and Methods: We combine two recently propo…
Cramér-Rao bound-informed training of neural networks for quantitative MRI
Xiaoxia Zhang, Quentin Duchemin, Kangning Liu +4
Neural networks are increasingly used to estimate parameters in quantitative MRI, in particular in magnetic resonance fingerprinting. Their advantages over the gold standard non-li…
Generalized Bloch model: a theory for pulsed magnetization transfer
Jakob Assländer, Cem Gultekin, Sebastian Flassbeck +2
Purpose: The paper introduces a classical model to describe the dynamics of large spin-1/2 ensembles associated with nuclei bound in large molecule structures, commonly referred to…