Add to ORKGAn optimal inductor design methodology using dimensioning models derived from Finite Element Analysis (FEA) supervised Artificial Neural Networks (ANN) is presented. The efficiency of such trained ANN dimensioning models in terms of compromise between precision and computing time is demonstrated for the cylindrical inductor topology with air and magnetic material core including saturation
Neural network applications in microwave engineering have been reported since the 1990s. Description...
Artificial neural networks [ANNs] are an effective method for predicting and classifying variables. ...
The artificial neural network (ANN)-based compact modeling methodology is evaluated in the context o...
A deep learning approach for the efficient electromagnetic analysis of an on-chip inductor with dumm...
This paper presents parameter and topology optimization of inductor shapes using evolutionary algori...
Recent studies proved that Ferrite Core (FC) power inductors working in sustainable saturation condi...
In this work, a topology optimization procedure is proposed and applied to the TEAM 25 problem, i.e....
The growing electrification of aircrafts required for the development of more electrical aircrafts n...
In this work a novel approach is presented for topology optimization of low frequency electromagneti...
A high-efficient wideband through-silicon vias (TSVs) modeling method based on deep learning is prop...
Purpose of this work is optimization of given electrical machine based on combination of selected me...
The main driver of aerospace structures design is the increase in performances of currently in use c...
ABSTRACT: A solenoidal structure for implementation of on-chip inductors is presented. An electromag...
This chapter reviews the intersection of two major CAD technologies for modeling and design of RF an...
Electromagnetics (EM) based device modeling and circuit optimization through Artificial Neural Netwo...
Neural network applications in microwave engineering have been reported since the 1990s. Description...
Artificial neural networks [ANNs] are an effective method for predicting and classifying variables. ...
The artificial neural network (ANN)-based compact modeling methodology is evaluated in the context o...
A deep learning approach for the efficient electromagnetic analysis of an on-chip inductor with dumm...
This paper presents parameter and topology optimization of inductor shapes using evolutionary algori...
Recent studies proved that Ferrite Core (FC) power inductors working in sustainable saturation condi...
In this work, a topology optimization procedure is proposed and applied to the TEAM 25 problem, i.e....
The growing electrification of aircrafts required for the development of more electrical aircrafts n...
In this work a novel approach is presented for topology optimization of low frequency electromagneti...
A high-efficient wideband through-silicon vias (TSVs) modeling method based on deep learning is prop...
Purpose of this work is optimization of given electrical machine based on combination of selected me...
The main driver of aerospace structures design is the increase in performances of currently in use c...
ABSTRACT: A solenoidal structure for implementation of on-chip inductors is presented. An electromag...
This chapter reviews the intersection of two major CAD technologies for modeling and design of RF an...
Electromagnetics (EM) based device modeling and circuit optimization through Artificial Neural Netwo...
Neural network applications in microwave engineering have been reported since the 1990s. Description...
Artificial neural networks [ANNs] are an effective method for predicting and classifying variables. ...
The artificial neural network (ANN)-based compact modeling methodology is evaluated in the context o...