We present a fast method for modeling three-dimensional low frequency controlled source electro-magnetic (CSEM) problems. We apply the method to the marine controlled source electromagnetic (MCSEM) exploration situation where conductivity and permittivity are different from the known background medium. For 3D problems fast computational methods are relevant for both forward and inverse modeling...

In today’s world, green human resource management is one of the most important factors in forward-thinking your environment-friendly business. Most of the researchers are of the view that employees must be empowered and environmentally aware of greening while carrying out green human resource management practices.  The present study is examining the impact of different Green human resource prac...

an artificial neural network has been used to determine the volume flux and rejections of ca2+ , na+ and cl¯, as a function of transmembrane pressure and concentrations of ca2+, polyethyleneimine, and polyacrylic acid in water softening by nanofiltration process in presence of polyelectrolytes. the feed-forward multi-layer perceptron artificial neural network including an eight-neuron hidden la...

Cracking of an all-trans n-alkane, via idealized Lewis acid and Bronsted acid catalysis, was examined using density functional theory. Optimized geometries and transitions states were determined for catalyst-reactant complexes, using AlCl3 and HCl.AlCl3 as the Lewis and Bronsted acids. For the Lewis acid cycle, hydride-transfer steps are seen to have large barriers in both forward and reverse d...

in this paper, first a great number of inverse problems which arise in instrumentation, in computer imaging systems and in computer vision are presented. then a common general forward modeling for them is given and the corresponding inversion problem is presented. then, after showing the inadequacy of the classical analytical and least square methods for these ill posed inverse problems, a baye...

This work formulates a new approach to reduced modeling of parameterized, time-dependent partial differential equations (PDEs). The method employs Operator Inference, scientific machine learning framework combining data-driven and physics-based modeling. parametric structure the governing is embedded directly into reduced-order model, parameterized operators are learned via linear regression pr...

This paper presents a new structure for adaptive predistortion of the memoryless, nonlinear saturation effects causedby High Power Amplifiers (HPA). Timely compensation for HPA distortions is critical for cost-effective prevention of the cliff effect during terrestrial transmission of digital broadcast television. The new structure results from a two-stage approach: The forward model is identif...