Deep-learning-based precise characterization of microwave transistors using fully-automated regression surrogates

N Calik, F Güneş, S Koziel, A Pietrenko-Dabrowska… - Scientific reports, 2023 - nature.com
Accurate models of scattering and noise parameters of transistors are instrumental in
facilitating design procedures of microwave devices such as low-noise amplifiers. Yet, data …

An intelligent antenna synthesis method based on machine learning

D Shi, C Lian, K Cui, Y Chen… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
An intelligent antenna synthesis method is proposed to automatically select suitable
antenna type and provide optimal geometric parameters according to the requirement of …

Data-driven surrogate-assisted optimization of metamaterial-based filtenna using deep learning

P Mahouti, A Belen, O Tari, MA Belen, S Karahan… - Electronics, 2023 - mdpi.com
In this work, a computationally efficient method based on data-driven surrogate models is
proposed for the design optimization procedure of a Frequency Selective Surface (FSS) …

Reliable computationally efficient behavioral modeling of microwave passives using deep learning surrogates in confined domains

S Koziel, N Çalık, P Mahouti… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
The importance of surrogate modeling techniques has been steadily growing over the recent
years in high-frequency electronics, including microwave engineering. Fast metamodels are …

Reduced-cost two-level surrogate antenna modeling using domain confinement and response features

A Pietrenko-Dabrowska, S Koziel, U Ullah - Scientific reports, 2022 - nature.com
Electromagnetic (EM) simulation tools have become indispensable in the design of
contemporary antennas. Still, the major setback of EM-driven design is the associated …

Deep reinforcement learning-assisted optimization for resource allocation in downlink OFDMA cooperative systems

MK Tefera, S Zhang, Z ** - Entropy, 2023 - mdpi.com
This paper considers a downlink resource-allocation problem in distributed interference
orthogonal frequency-division multiple access (OFDMA) systems under maximal power …

Optimal sampling-based neural networks for uncertainty quantification and stochastic optimization

S Gupta, A Paudel, M Thapa, SB Mulani… - Aerospace Science and …, 2023 - Elsevier
In recent times, neural networks (NN) have been successfully utilized to model real-world
engineering problems. However, complexities in constructing an optimal NN architecture …

Data driven surrogate modeling of horn antennas for optimal determination of radiation pattern and size using deep learning

OC Piltan, A Kizilay, MA Belen… - Microwave and Optical …, 2024 - Wiley Online Library
Horn antenna designs are favored in many applications where ultra‐wide‐band operation
range alongside of a high‐performance radiation pattern characteristics are requested …

[HTML][HTML] Knowledge-based turbomachinery design system via a deep neural network and multi-output Gaussian process

J Chen, C Liu, L Xuan, Z Zhang, Z Zou - Knowledge-Based Systems, 2022 - Elsevier
The requirements of future aeroengines challenge turbomachinery designs to be quieter,
greener, and more efficient; furthermore, they must be developed at considerably reduced …