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comsol multiphysics  (COMSOL Inc)
90
COMSOL Inc comsol multiphysics
Optimization workflow for MN design . ( a ) Defining key parameters that affect the overall performance of MNs, such as their length, inlet diameter, outlet diameter, wall thickness, and Bessel curve parameters. ( b ) Converting the MN model created in COMSOL <t>Multiphysics</t> into a MATLAB script (.m file). This process takes design parameters as inputs and outputs the volumetric flow rate (VFR), laying the groundwork for subsequent optimization algorithms. ( c ) Applying Bayesian optimization, this algorithm uses the model function as the objective function to systematically adjust parameters and explore through a set number of iterations, aiming to discover parameter combinations that maximize the VFR. ( d ) Assessing the effects of different hyperparameter combinations in Bayesian optimization and identifying the optimal parameter set to achieve the maximum VFR.
Comsol Multiphysics, supplied by COMSOL Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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comsol multiphysics - by Bioz Stars, 2026-05
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machine learning toolbox  (MathWorks Inc)
96
MathWorks Inc machine learning toolbox
Optimization workflow for MN design . ( a ) Defining key parameters that affect the overall performance of MNs, such as their length, inlet diameter, outlet diameter, wall thickness, and Bessel curve parameters. ( b ) Converting the MN model created in COMSOL <t>Multiphysics</t> into a MATLAB script (.m file). This process takes design parameters as inputs and outputs the volumetric flow rate (VFR), laying the groundwork for subsequent optimization algorithms. ( c ) Applying Bayesian optimization, this algorithm uses the model function as the objective function to systematically adjust parameters and explore through a set number of iterations, aiming to discover parameter combinations that maximize the VFR. ( d ) Assessing the effects of different hyperparameter combinations in Bayesian optimization and identifying the optimal parameter set to achieve the maximum VFR.
Machine Learning Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/machine learning toolbox/product/MathWorks Inc
Average 96 stars, based on 1 article reviews
machine learning toolbox - by Bioz Stars, 2026-05
96/100 stars
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Optimization workflow for MN design . ( a ) Defining key parameters that affect the overall performance of MNs, such as their length, inlet diameter, outlet diameter, wall thickness, and Bessel curve parameters. ( b ) Converting the MN model created in COMSOL Multiphysics into a MATLAB script (.m file). This process takes design parameters as inputs and outputs the volumetric flow rate (VFR), laying the groundwork for subsequent optimization algorithms. ( c ) Applying Bayesian optimization, this algorithm uses the model function as the objective function to systematically adjust parameters and explore through a set number of iterations, aiming to discover parameter combinations that maximize the VFR. ( d ) Assessing the effects of different hyperparameter combinations in Bayesian optimization and identifying the optimal parameter set to achieve the maximum VFR.

Journal: Biomimetics

Article Title: Machine Learning Assists in the Design and Application of Microneedles

doi: 10.3390/biomimetics9080469

Figure Lengend Snippet: Optimization workflow for MN design . ( a ) Defining key parameters that affect the overall performance of MNs, such as their length, inlet diameter, outlet diameter, wall thickness, and Bessel curve parameters. ( b ) Converting the MN model created in COMSOL Multiphysics into a MATLAB script (.m file). This process takes design parameters as inputs and outputs the volumetric flow rate (VFR), laying the groundwork for subsequent optimization algorithms. ( c ) Applying Bayesian optimization, this algorithm uses the model function as the objective function to systematically adjust parameters and explore through a set number of iterations, aiming to discover parameter combinations that maximize the VFR. ( d ) Assessing the effects of different hyperparameter combinations in Bayesian optimization and identifying the optimal parameter set to achieve the maximum VFR.

Article Snippet: Specifically, they model the fluid behavior in MN patches using COMSOL Multiphysics, and optimize parameters such as length, inlet diameter, outlet diameter, thickness, and the Bézier curve that defines the concave profile of the MN using MATLAB’s Bayesian optimization algorithm “bayesopt” function ( ).

Techniques: