scholarly journals Identification of release sources in advection–diffusion system by machine learning combined with Green’s function inverse method

2018 ◽  
Vol 60 ◽  
pp. 64-76 ◽  
Author(s):  
Valentin G. Stanev ◽  
Filip L. Iliev ◽  
Scott Hansen ◽  
Velimir V. Vesselinov ◽  
Boian S. Alexandrov
Keyword(s):  
Machine Learning ◽  
Green’S Function ◽  
Green's Function ◽  
Inverse Method ◽  
Diffusion System ◽  
Advection Diffusion ◽  
Function Inverse

Theory of machine learning based on nonrelativistic quantum mechanics

2021 ◽  
pp. 2141004
Author(s):  
Huber Nieto-Chaupis
Keyword(s):  
Machine Learning ◽  
Quantum Mechanics ◽  
Green’S Function ◽  
Green's Function ◽  
Evolution Operator ◽  
Systematic Errors ◽  
Nonrelativistic Quantum ◽  
Quantum Learning ◽  
Nonrelativistic Quantum Mechanics

The goal of this paper is the presentation of the elementary procedures that normally are done in nonrelativistic Quantum Mechanics in terms of the principles of Machine Learning. In essence, this paper discusses Mitchell’s criteria, whose block fundamental dictates that the universal evolution of any system is composed by three fundamental steps: (i) Task, (ii) Performance and (iii) Experience. In this paper, the quantum mechanics formalism reflected on the usage of evolution operator and Green’s function are assumed to be part of mechanisms that are inherently engaged to the Machine Learning philosophy. The action for measuring observables through experiments and the intrinsic apparition of statistical or systematic errors are discussed in terms of “quantum learning”.

GREEN'S FUNCTION MATCHING METHOD FOR REALISTIC CALCULATIONS OF INTERFACES

1985 ◽  
Vol 46 (C4) ◽  
pp. C4-321-C4-329 ◽  
Author(s):  
E. Molinari ◽  
G. B. Bachelet ◽  
M. Altarelli
Keyword(s):  
Green’S Function ◽  
Green's Function ◽  
Matching Method
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