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

Factorial Design02:01

Factorial Design

13.7K
Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
13.7K
Upsampling01:22

Upsampling

581
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
581
Modeling and Similitude01:12

Modeling and Similitude

613
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
613
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.1K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.1K
Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

1.3K
A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...
1.3K
Response Surface Methodology01:16

Response Surface Methodology

604
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
604

You might also read

Related Articles

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

Sort by
Same author

Preserving bare mudflats reduces methane emissions: Implications for coastal wetland management.

Journal of environmental management·2026
Same author

A review of the application of novel intervertebral disc diagnostic technologies integrated with artificial intelligence in medical imaging.

Digital health·2026
Same author

Cuproptosis-immunity crosstalk informs strategy to overcome immunotherapy resistance.

Cell·2026
Same author

Multimorbidity Patterns and Cognitive Transitions Among the Elderly in China: The Longitudinal Evidence From CLHLS.

Asia-Pacific journal of public health·2026
Same author

Haplotype-resolved methylomes reveal parent-of-origin DNA methylation imbalance in autism spectrum disorder.

Science advances·2026
Same author

An Autocatalytic Molecular Sensor Enables Rapid, Sensitive, and One-Pot Detection of Nucleic Acids.

ACS nano·2026

Related Experiment Video

Updated: Jan 15, 2026

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

1.8K

Higher-order factorization machine for accurate surrogate modeling in material design.

Sanghyo Hwang1, Seongmin Kim2, Zhihao Xu3

  • 1Department of Electronic Engineering, Kyung Hee University, Yongin-Si, Gyeonggi-do, 17104, Republic of Korea.

Scientific Reports
|October 9, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a 3rd-order factorization machine (FM) model for active learning in material science. This advanced model improves optimization accuracy and efficiency over 2nd-order FM for complex material design challenges.

Keywords:
Factorization machineHigher-order interactionsMachine learningMaterial designOptimization

More Related Videos

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

2.0K
Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

17.1K

Related Experiment Videos

Last Updated: Jan 15, 2026

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

1.8K
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

2.0K
Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

17.1K

Area of Science:

  • Material Science
  • Computational Chemistry
  • Data Science

Background:

  • Efficient optimization is crucial for material science, driving the use of surrogate-based active learning.
  • Second-order factorization machine (FM) models are common surrogates but struggle with complex variable interactions.

Purpose of the Study:

  • To develop and evaluate an active learning scheme using a 3rd-order FM model.
  • To enhance the modeling of higher-order interactions in material systems for improved optimization.

Main Methods:

  • Implemented an active learning framework incorporating a 3rd-order FM surrogate model.
  • Evaluated surrogate model performance across various objective functions.
  • Assessed optimization reliability and efficiency in a nanophotonic structure design task.

Main Results:

  • The 3rd-order FM demonstrated superior surrogate modeling accuracy compared to the 2nd-order FM.
  • Active learning with the 3rd-order FM achieved better optimization performance.
  • Higher-order FM models show significant promise for material design.

Conclusions:

  • Third-order factorization machines offer enhanced capabilities for modeling complex relationships in material science.
  • The proposed active learning approach with 3rd-order FM improves material design and optimization efficiency.