Informed machine learning–a taxonomy and survey of integrating prior knowledge into learning systems
Despite its great success, machine learning can have its limits when dealing with insufficient
training data. A potential solution is the additional integration of prior knowledge into the …
training data. A potential solution is the additional integration of prior knowledge into the …
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 …
Multi-fidelity physics-constrained neural network and its application in materials modeling
D Liu, Y Wang - Journal of Mechanical Design, 2019 - asmedigitalcollection.asme.org
Training machine learning tools such as neural networks require the availability of sizable
data, which can be difficult for engineering and scientific applications where experiments or …
data, which can be difficult for engineering and scientific applications where experiments or …
Machine learning for structural health monitoring: challenges and opportunities
A physics-based approach to structural health monitoring (SHM) has practical shortcomings
which restrict its suitability to simple structures under well controlled environments. With the …
which restrict its suitability to simple structures under well controlled environments. With the …
A review on the design and optimization of antennas using machine learning algorithms and techniques
HM El Misilmani, T Naous… - International Journal of …, 2020 - Wiley Online Library
This paper presents a focused and comprehensive literature survey on the use of machine
learning (ML) in antenna design and optimization. An overview of the conventional …
learning (ML) in antenna design and optimization. An overview of the conventional …
Space mapping: the state of the art
We review the space-mapping (SM) technique and the SM-based surrogate (modeling)
concept and their applications in engineering design optimization. For the first time, we …
concept and their applications in engineering design optimization. For the first time, we …
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 …
Deep-learning-assisted physics-driven MOSFET current-voltage modeling
In this work, we propose using deep learning to improve the accuracy of the partially-physics-
based conventional MOSFET current-voltage model. The benefits of having some physics …
based conventional MOSFET current-voltage model. The benefits of having some physics …
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 …