Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Reducing Line Loss01:18

Reducing Line Loss

149
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
149
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

2.5K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
2.5K
Classification of Signals01:30

Classification of Signals

418
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
418
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.5K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.5K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.3K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.3K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

45
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
45

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Secular Trend and Socioeconomic Variation in Body Height of Young Polish Men Between 1965 and 2023.

American journal of human biology : the official journal of the Human Biology Council·2026
Same author

Simplex-anchored regressors for fast global optimization-oriented miniaturization of microwave circuits.

Scientific reports·2026
Same author

Experimentally validated dual-band GHz metamaterial perfect absorber biosensor with negative-index response and AI-assisted electromagnetic analysis for breast cancer dielectric discrimination.

Biosensors & bioelectronics·2026
Same author

Sequential rescue strategy in refractory acute esophageal variceal bleeding: a retrospective study.

Polish archives of internal medicine·2026
Same author

Design and experimental validation of multi-section directional coupler with arbitrary coupling and high directivity for sub-6 GHz UWB applications.

PloS one·2026
Same author

Fabry-Perot cavity-based circularly polarized Sierpinski fractal antenna with analysis and measurement characterization for sub 5/6G V2I and V2V communication.

Scientific reports·2026

Related Experiment Video

Updated: Jun 12, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

981

Antenna optimization using machine learning with reduced-dimensionality surrogates.

Slawomir Koziel1,2, Anna Pietrenko-Dabrowska3, Leifur Leifsson4

  • 1Engineering Optimization and Modeling Center, Reykjavik University, 101, Reykjavík, Iceland. koziel@ru.is.

Scientific Reports
|September 18, 2024
PubMed
Summary

This study presents a novel machine learning approach for rapid antenna optimization, significantly reducing computational costs. The method uses a reduced-dimensionality surrogate model and particle swarm optimization for efficient antenna design.

Keywords:
AntennasEM-based designGlobal searchNature-inspired algorithmsSensitivity analysisSurrogate modeling

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.7K

Related Experiment Videos

Last Updated: Jun 12, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

981
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.7K

Area of Science:

  • Electromagnetics
  • Antenna Theory
  • Computational Intelligence

Background:

  • Modern antenna design demands high performance and functionality, requiring intricate structures and precise parameter adjustments.
  • Conventional optimization methods using full-wave electromagnetic (EM) simulations are computationally expensive.
  • Surrogate-based techniques face challenges with high dimensionality and nonlinear antenna responses.

Purpose of the Study:

  • To introduce an innovative and swift technique for antenna optimization.
  • To develop a computationally efficient method that overcomes the limitations of existing approaches.
  • To enhance the speed and accuracy of antenna design processes.

Main Methods:

  • A machine learning framework utilizing kriging for surrogate modeling.
  • A particle swarm optimizer as the primary search engine.
  • Dimensionality reduction guided by fast global sensitivity analysis and supplemented by local sensitivity-based adjustment.

Main Results:

  • The developed technique significantly reduces computational costs for antenna optimization.
  • The surrogate model operates effectively in a reduced-dimensionality domain.
  • Comparative experiments show competitive performance against full-dimensionality machine learning and direct EM-driven bio-inspired methods.

Conclusions:

  • The proposed technique offers a computationally efficient and effective solution for antenna optimization.
  • Reduced-dimensionality modeling combined with sensitivity analysis enhances metamodel dependability.
  • This approach provides a viable alternative for complex antenna design challenges.