Connected‐diagram expansions of effective Hamiltonians in incomplete model spaces. II. The general incomplete model space

1987 ◽  
Vol 87 (10) ◽  
pp. 5911-5916 ◽  
Author(s):  
Debashis Mukherjee ◽  
Werner Kutzelnigg ◽  
Sigurd Koch
Keyword(s):  
Model Space ◽  
Incomplete Model ◽  
Model Spaces ◽  
Connected Diagram ◽  
Effective Hamiltonians

scholarly journals FORMAL RELATION AMONG VARIOUS HERMITIAN AND NON-HERMITIAN EFFECTIVE INTERACTIONS

2005 ◽  
Vol 14 (01) ◽  
pp. 21-28 ◽  
Author(s):  
RYOJI OKAMOTO ◽  
SHINICHIRO FUJII ◽  
KENJI SUZUKI
Keyword(s):  
Effective Interaction ◽  
Model Space ◽  
General Definition ◽  
Effective Interactions ◽  
Formal Relation ◽  
Definition Of ◽  
Effective Hamiltonians

A general definition of the model-space effective interaction is given. The energy-independent effective Hamiltonians derived in a time-independent way are classified systematically.

Coupled-cluster method for an incomplete model space

1992 ◽  
Vol 44 (S26) ◽  
pp. 107-115 ◽  
Author(s):  
Stanislaw A. Kucharski ◽  
Rodney J. Bartlett
Keyword(s):  
Model Space ◽  
Cluster Method ◽  
Coupled Cluster ◽  
Coupled Cluster Method ◽  
Incomplete Model

Bayesian subsampling of time-domain electromagnetic data using kernel density product

Geophysics ◽  
2021 ◽  
pp. 1-45
Author(s):  
Hai Li ◽  
Guoqiang Xue ◽  
Wen Chen
Keyword(s):  
Time Domain ◽  
Kernel Density ◽  
Model Space ◽  
Drill Hole ◽  
Divide And Conquer ◽  
Bayesian Inversion ◽  
Model Parameters ◽  
Model Spaces ◽  
Time Domain Electromagnetic ◽  
Speed Up

The Bayesian method is a powerful tool to estimate the resistivity distribution and associate uncertainty from time-domain electromagnetic (TDEM) data. As the forward simulation of the TDEM method is computationally expensive and a large number of samples are needed to globally explore the model space, the full Bayesian inversion of TDEM data is limited to layered models. To make high-dimensional Bayesian inversion tractable, we propose a divide-and-conquer strategy to speed up the Bayesian inversion of TDEM data. First, the full datasets and model spaces are divided into disjoint batches based on the coverage of the sources so that independent and highly efficient Bayesian subsampling can be conducted. Then, the samples from each subsampling procedure are combined to get the full posterior. To obtain an asymptotically unbiased approximation to the full posterior, a kernel density product method is used to reintegrate samples from each subposterior. The model parameters and their uncertainty are estimated from the full posterior. The proposed method is tested on synthetic examples and applied to a field dataset acquired with a large fixed-loop configuration. The 2D section from the Bayesian inversion revealed several mineralized zones, one of which matches well with the information from a nearby drill hole. The field example shows the ability of Bayesian inversion to infer reliable resistivity and uncertainty.

A size-extensive state-specific multi-reference many-body approach using incomplete model spaces

2003 ◽  
Vol 381 (1-2) ◽  
pp. 223-229 ◽  
Author(s):  
Dola Pahari ◽  
Sudip Chattopadhyay ◽  
Sanghamitra Das ◽  
Debashis Mukherjee
Keyword(s):  
Many Body ◽  
Incomplete Model ◽  
Model Spaces
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