'Field approximation' presentation slideshows

SUPERCONDUCTIVITY IN SYSTEMS WITH STRONG CORRELATIONS

SUPERCONDUCTIVITY IN SYSTEMS WITH STRONG CORRELATIONS. N.M.Plakida Joint Institute for Nuclear Research, Dubna, Russia. Dubna, 28.08.2006. Outline. ? Dense matter vs Strong correlations ? Weak and strong corelations ? Cuprate superconductors ? Effective p-d Hubbard model

563 views • 41 slides

Isovector pairing in nuclei near the N=Z line

Isovector pairing in nuclei near the N=Z line. Anatoli Afanasjev S. Frauendorf Kai Neergard J. Sheikh. Mean-field theory of isovector pairing. Frauendorf SG , Sheikh JA Cranked shell model and isospin symmetry near N=Z NUCLEAR PHYSICS A 645 (4): 509-535 JAN 25 1999.

453 views • 32 slides

Asymptotic Behavior of Stochastic Complexity of Complete Bipartite Graph-Type Boltzmann Machines

Asymptotic Behavior of Stochastic Complexity of Complete Bipartite Graph-Type Boltzmann Machines. Yu Nishiyama and Sumio Watanabe Tokyo Institute of Technology, Japan. Background. Learning machines. Information systems. Pattern recognition. Mixture models. Natural language processing.

186 views • 18 slides

An investigation of ceria's oxygen storage capacity by computer simulation

270 views • 17 slides

Compact stars in the QCD phase diagram II,2009

Compact stars in the QCD phase diagram II,2009. Pion Superfluidity beyond Mean Field Approximation In the Nambu-Jona-Lasinio Model Chengfu Mu Tsinghua University, Beijing. Collaborator: Professor Pengfei Zhuang Ref: Phys.Rev.D79, 094006(2009).

145 views • 14 slides

Prof. David R. Jackson ECE Dept.

ECE 6341. Spring 2016. Prof. David R. Jackson ECE Dept. Notes 16. Dipole Scattering. z. y. x. Note: We can replace z with z - z ' and ? with ? - ? ' at the end, if we wish. Dipole at origin (from Notes 15):. Dipole Scattering (cont.). Dipole at.

224 views • 12 slides

Improving the Mean Field Approximation via the Use of Mixture Distributions

Improving the Mean Field Approximation via the Use of Mixture Distributions. Tommi S. Jaakola Michael I. Jordan. 1. Introduction. Inference in Graphical Model make use of conditional dependency with Markov properties : sparse graph without long cycles

87 views • 8 slides