Application of the linear and nonlinear inversion algorithms on two-dimensional experimental electromagnetic data

2009 ◽  
Author(s):  
Jinghong Miao ◽  
R. Marklein ◽  
Jianxiong Li
Keyword(s):  
Nonlinear Inversion ◽  
Two Dimensional ◽  
Electromagnetic Data ◽  
Linear And Nonlinear

scholarly journals Asymptotic estimates of the convergence of classical Schwarz waveform relaxation domain decomposition methods for two-dimensional stationary quantum waves

2018 ◽  
Vol 52 (4) ◽  
pp. 1569-1596 ◽  
Author(s):  
Xavier Antoine ◽  
Fengji Hou ◽  
Emmanuel Lorin
Keyword(s):  
Domain Decomposition ◽  
Decomposition Methods ◽  
Nonlinear Schrödinger Equations ◽  
Waveform Relaxation ◽  
Schrödinger Equations ◽  
Two Dimensional ◽  
Domain Decomposition Methods ◽  
Schwarz Waveform Relaxation ◽  
Nonlinear Schrodinger Equations ◽  
Linear And Nonlinear

This paper is devoted to the analysis of convergence of Schwarz Waveform Relaxation (SWR) domain decomposition methods (DDM) for solving the stationary linear and nonlinear Schrödinger equations by the imaginary-time method. Although SWR are extensively used for numerically solving high-dimensional quantum and classical wave equations, the analysis of convergence and of the rate of convergence is still largely open for linear equations with variable coefficients and nonlinear equations. The aim of this paper is to tackle this problem for both the linear and nonlinear Schrödinger equations in the two-dimensional setting. By extending ideas and concepts presented earlier [X. Antoine and E. Lorin, Numer. Math. 137 (2017) 923–958] and by using pseudodifferential calculus, we prove the convergence and determine some approximate rates of convergence of the two-dimensional Classical SWR method for two subdomains with smooth boundary. Some numerical experiments are also proposed to validate the analysis.

scholarly journals SCATTERING ANALYSIS FOR SHIP KELVIN WAKES ON TWO-DIMENSIONAL LINEAR AND NONLINEAR SEA SURFACES

2013 ◽  
Vol 52 ◽  
pp. 405-423
Author(s):  
Rong-Qing Sun ◽  
Min Zhang ◽  
Chao Wang ◽  
Xiao-Feng Yuan
Keyword(s):  
Two Dimensional ◽  
Linear And Nonlinear

The Nonlinear Optical Transition Bleaching in Tellurene

Nanoscale ◽  
2021 ◽  
Author(s):  
Yiduo Wang ◽  
Yingwei Wang ◽  
Yulan Dong ◽  
Li Zhou ◽  
Hao Wei ◽  
...  
Keyword(s):  
Optical Properties ◽  
Optical Anisotropy ◽  
Nonlinear Optical ◽  
Optical Transition ◽  
Nonlinear Optical Properties ◽  
Two Dimensional ◽  
Linear And Nonlinear

To date, outstanding linear and nonlinear optical properties of tellurene, caused by multiple two-dimensional (2D) phases and optical anisotropy, have been attracted considerable interest for potential nanophotonics applications. In this...

Linear and nonlinear electrical conduction in quasi-two-dimensional quantum wells

1987 ◽  
Vol 35 (3) ◽  
pp. 1334-1344 ◽  
Author(s):  
P. Vasilopoulos ◽  
M. Charbonneau ◽  
C. Van Vliet
Keyword(s):  
Quantum Wells ◽  
Electrical Conduction ◽  
Two Dimensional ◽  
Linear And Nonlinear

A generic 1-D imaging method for transient electromagnetic data

Geophysics ◽  
2002 ◽  
Vol 67 (2) ◽  
pp. 438-447 ◽  
Author(s):  
Niels Bøie Christensen
Keyword(s):  
Time Derivative ◽  
Step Response ◽  
Imaging Method ◽  
Nonlinear Inversion ◽  
Inversion Algorithm ◽  
Electromagnetic Data ◽  
Transient Electromagnetic ◽  
Dependent Part ◽  
Space Step ◽  
Forward Mapping

This paper presents a fast approximate 1-D inversion algorithm for transient electromagnetic (EM) data that can be applied for all measuring configurationsand transmitter waveforms and for all field components. The inversion is based on an approximate forward mapping in the adaptive Born approximation. The generality is obtained through a separation of the forward problem into a configuration-independent part, mapping layer conductivities into apparent conductivity, and a configuration-dependent part, the half-space step response. The EM response from any waveform can then be found by a convolution with the time derivative of the waveform. The approach does not involve inherently unstable deconvolution computations or nonunique transformations, and it is about 100 times faster than ordinary nonlinear inversion. Nonlinear model responses of the models obtained through the approximate inversion fit the data typically within 5%.

Linear and nonlinear inversion algorithms applied in nondestructive evaluation

2002 ◽  
Vol 18 (6) ◽  
pp. 1733-1759 ◽  
Author(s):  
R Marklein ◽  
K Mayer ◽  
R Hannemann ◽  
T Krylow ◽  
K Balasubramanian ◽  
...  
Keyword(s):  
Nondestructive Evaluation ◽  
Nonlinear Inversion ◽  
Linear And Nonlinear
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