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Deep-learning-based precise characterization of microwave transistors using fully-automated regression surrogates
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 …
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 …
antenna type and provide optimal geometric parameters according to the requirement of …
Data-driven surrogate-assisted optimization of metamaterial-based filtenna using deep learning
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) …
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
The importance of surrogate modeling techniques has been steadily growing over the recent
years in high-frequency electronics, including microwave engineering. Fast metamodels are …
years in high-frequency electronics, including microwave engineering. Fast metamodels are …
Reduced-cost two-level surrogate antenna modeling using domain confinement and response features
Electromagnetic (EM) simulation tools have become indispensable in the design of
contemporary antennas. Still, the major setback of EM-driven design is the associated …
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 …
orthogonal frequency-division multiple access (OFDMA) systems under maximal power …
Optimal sampling-based neural networks for uncertainty quantification and stochastic optimization
In recent times, neural networks (NN) have been successfully utilized to model real-world
engineering problems. However, complexities in constructing an optimal NN architecture …
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
Horn antenna designs are favored in many applications where ultra‐wide‐band operation
range alongside of a high‐performance radiation pattern characteristics are requested …
range alongside of a high‐performance radiation pattern characteristics are requested …
Improved efficacy behavioral modeling of microwave circuits through dimensionality reduction and fast global sensitivity analysis
Behavioral models have garnered significant interest in the realm of high-frequency
electronics. Their primary function is to substitute costly computational tools, notably …
electronics. Their primary function is to substitute costly computational tools, notably …
[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 …
greener, and more efficient; furthermore, they must be developed at considerably reduced …