Deep neural networks for the evaluation and design of photonic devices
The data-science revolution is poised to transform the way photonic systems are simulated
and designed. Photonic systems are, in many ways, an ideal substrate for machine learning …
and designed. Photonic systems are, in many ways, an ideal substrate for machine learning …
Deep learning for the design of photonic structures
Innovative approaches and tools play an important role in shaping design, characterization
and optimization for the field of photonics. As a subset of machine learning that learns …
and optimization for the field of photonics. As a subset of machine learning that learns …
Deep learning the electromagnetic properties of metamaterials—a comprehensive review
Deep neural networks (DNNs) are empirically derived systems that have transformed
traditional research methods, and are driving scientific discovery. Artificial electromagnetic …
traditional research methods, and are driving scientific discovery. Artificial electromagnetic …
Artificial neural networks for microwave computer-aided design: The state of the art
This article presents an overview of artificial neural network (ANN) techniques for a
microwave computer-aided design (CAD). ANN-based techniques are becoming useful for …
microwave computer-aided design (CAD). ANN-based techniques are becoming useful for …
Artificial neural networks for RF and microwave design-from theory to practice
QJ Zhang, KC Gupta… - IEEE transactions on …, 2003 - ieeexplore.ieee.org
Neural-network computational modules have recently gained recognition as an
unconventional and useful tool for RF and microwave modeling and design. Neural …
unconventional and useful tool for RF and microwave modeling and design. Neural …
EM-based optimization of microwave circuits using artificial neural networks: The state-of-the-art
JE Rayas-Sánchez - IEEE Transactions on Microwave Theory …, 2004 - ieeexplore.ieee.org
This paper reviews the current state-of-the-art in electromagnetic (EM)-based design and
optimization of microwave circuits using artificial neural networks (ANNs). Measurement …
optimization of microwave circuits using artificial neural networks (ANNs). Measurement …
Space mapping
Microwave CAD has its roots in the 1960s [1]. Its practice saw the enrichment of circuit-
based model libraries, advances in EM and circuit simulation accuracy, and the refinement …
based model libraries, advances in EM and circuit simulation accuracy, and the refinement …
[HTML][HTML] Prediction of pile axial bearing capacity using artificial neural network and random forest
Axial bearing capacity of piles is the most important parameter in pile foundation design. In
this paper, artificial neural network (ANN) and random forest (RF) algorithms were utilized to …
this paper, artificial neural network (ANN) and random forest (RF) algorithms were utilized to …
Neural network inverse modeling and applications to microwave filter design
In this paper, systematic neural network modeling techniques are presented for microwave
modeling and design using the concept of inverse modeling where the inputs to the inverse …
modeling and design using the concept of inverse modeling where the inputs to the inverse …
Smart modeling of microwave devices
Modeling and computer-aided design (CAD) techniques are essential for microwave design,
especially with the drive towards first-pass design success. We have described neural …
especially with the drive towards first-pass design success. We have described neural …