The deconvolution of the quasar structure from the lightcurve of a high amplification event with the regularisation method

2006 ◽  
pp. 198-202
Author(s):  
B. Grieger
Keyword(s):  
Regularisation Method

A new regularisation method for parameter identification in elliptic problems

1990 ◽  
Vol 6 (3) ◽  
pp. 465-477 ◽  
Author(s):  
U Tautenhahn
Keyword(s):  
Parameter Identification ◽  
Elliptic Problems ◽  
Regularisation Method

Quantum field theory on fractal spacetime: a new regularisation method

1987 ◽  
Vol 20 (12) ◽  
pp. 3861-3875 ◽  
Author(s):  
K Svozil
Keyword(s):  
Field Theory ◽  
Quantum Field Theory ◽  
Quantum Field ◽  
Regularisation Method

scholarly journals A Symmetric Prior for the Regularisation of Elastic Deformations: Improved Anatomical Plausibility in Nonlinear Image Registration

2019 ◽  
Author(s):  
Frederik J Lange ◽  
John Ashburner ◽  
Stephen M Smith ◽  
Jesper L R Andersson
Keyword(s):  
Image Registration ◽  
State Of The Art ◽  
Frame Of Reference ◽  
Elastic Deformations ◽  
Ill Posed ◽  
Computationally Intensive ◽  
Cortical Regions ◽  
Inverse Consistency ◽  
Regularisation Method

AbstractNonlinear registration is critical to many aspects of Neuroimaging research. It facilitates averaging and comparisons across multiple subjects, as well as reporting of data in a common anatomical frame of reference. It is, however, a fundamentally ill-posed problem, with many possible solutions which minimise a given dissimilarity metric equally well. We present a novel regularisation method that aims to selectively drive solutions towards those which would be considered anatomically plausible by penalising unlikely lineal, areal and volumetric deformations. In addition, our penalty is symmetric in the sense that geometric expansions and contractions are penalised equally, which encourages inverse-consistency. We demonstrate that our method is able to significantly reduce volume and shape distortions compared to state-of-the-art elastic (FNIRT) and plastic (ANTs) registration frameworks. Crucially, this is achieved whilst matching or exceeding the registration quality of these methods, as measured by overlap scores of labelled cortical regions. Furthermore, extensive use of GPU parallelisation has allowed us to implement what is a highly computationally intensive optimisation strategy while maintaining reasonable run times of under half an hour.

scholarly journals SPH as a nonlocal regularisation method: Solution for instabilities due to strain-softening

2014 ◽  
Vol 277 ◽  
pp. 281-304 ◽  
Author(s):  
R. Vignjevic ◽  
N. Djordjevic ◽  
S. Gemkow ◽  
T. De Vuyst ◽  
J. Campbell
Keyword(s):  
Strain Softening ◽  
Regularisation Method

Interconversion between viscoelastic functions using the Tikhonov regularisation method and its comparison with approximate techniques

2014 ◽  
Vol 15 (4) ◽  
pp. 820-840 ◽  
Author(s):  
Milad Ghorban Ebrahimi ◽  
Mofreh Saleh ◽  
Miguel A Moyers Gonzalez
Keyword(s):  
Tikhonov Regularisation ◽  
Regularisation Method
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