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
  1. Home
  3. Using Adaptive Surrogate Models To Accelerate Multi-objective Design Optimization Of Mems.
  1. Home
  3. Using Adaptive Surrogate Models To Accelerate Multi-objective Design Optimization Of Mems.

Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

101
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...
101
Response Surface Methodology01:16

Response Surface Methodology

267
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:
267
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

756
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
756
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

150
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
150

You might also read

Related Articles

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

Sort by
Same authorSame journal

A Hybrid Preprocessing Multi-Objective Surrogate Model for Thermal MEMS Actuators.

Micromachines·2026
Same author

Impact of Solid Materials in the Gap Space between Driving Electrodes in a MEMS Tri-Electrode Electrostatic Actuator.

Sensors (Basel, Switzerland)·2024
Same author

Prior knowledge guided eQTL mapping for identifying candidate genes.

BMC bioinformatics·2016
Same journal

Correction: Kang et al. Fluid Flow to Electricity: Capturing Flow-Induced Vibrations with Micro-Electromechanical-System-Based Piezoelectric Energy Harvester. <i>Micromachines</i> 2024, <i>15</i>, 581.

Micromachines·2026
Same journal

Femtosecond Laser Texturing of Wood Coatings with Bio-Based Epoxy and Wax Additives for Enhanced Hydrophobicity.

Micromachines·2026
Same journal

Engineering of Optoelectronic Devices for Renewable Energy Applications.

Micromachines·2026
Same journal

Phase Transformation and Electrochemical Behavior of Hexagonal TiO<sub>2</sub> Nanotubes Under Different Annealing Temperatures and Heating Rates.

Micromachines·2026
Same journal

Process Optimization and Predictive Modeling of Femtosecond Laser Precision Milling for Commercial PMMA Slices.

Micromachines·2026
See all related articles

Related Experiment Video

Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters
15:25

Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters

Published on: February 4, 2018

6.2K

Using Adaptive Surrogate Models to Accelerate Multi-Objective Design Optimization of MEMS.

Ali Nazari1, Armin Aghajani1, Phiona Buhr1

  • 1Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada.

Micromachines
|July 30, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces an adaptive optimization framework for micro-electromechanical systems (MEMS). Adaptive methods significantly improve solution quality and quantity while drastically reducing computational costs compared to traditional approaches.

Keywords:
Gaussian process regressionLorentz force actuatorMEMSdesign optimizationfinite element methodmulti-objective optimizationonline learningsurrogate modelingthermal actuator

More Related Videos

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

5.0K
Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

1.2K

Related Experiment Videos

Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters
15:25

Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters

Published on: February 4, 2018

6.2K
A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

5.0K
Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

1.2K

Area of Science:

  • Engineering
  • Computational Science

Background:

  • Traditional optimization methods for micro-electromechanical systems (MEMS) face limitations in handling objective constraints and providing diverse design options.
  • Existing techniques often require extensive computational resources, such as finite element method (FEM) simulations, limiting their practical application.

Purpose of the Study:

  • To present a comprehensive multi-objective optimization framework for MEMS design.
  • To integrate and evaluate traditional and adaptive optimization techniques, specifically Surrogate-Assisted Multi-Objective Optimization (SAMOO) and Adaptive-SAMOO (A-SAMOO).
  • To enhance the flexibility, applicability, and efficiency of MEMS optimization processes.

Main Methods:

  • Development of a multi-objective optimization framework incorporating both traditional and adaptive strategies.
  • Implementation of Surrogate-Assisted Multi-Objective Optimization (SAMOO) and its adaptive variant (A-SAMOO).
  • Testing and validation of the proposed methods on various MEMS devices, including Lorentz force and thermal actuators, with discrete design variables and objective constraints.

Main Results:

  • Adaptive optimization (A-SAMOO) significantly outperforms traditional methods in delivering a greater number and higher quality of optimal solutions.
  • The adaptive approach achieved high-quality solutions using only 2.8% of the FEM evaluations required by traditional methods without surrogate models.
  • Demonstrated robustness and versatility across different MEMS devices and complex design scenarios, including discrete variables and strict constraints.

Conclusions:

  • The proposed adaptive optimization framework enhances accuracy, speed, and solution diversity in MEMS design.
  • Online learning within the adaptive framework offers substantial advantages over traditional offline optimization techniques.
  • The framework provides a valuable, reliable, and effective resource for researchers and practitioners optimizing MEMS designs.