Parameterized Surrogate Models of Electromagnetic Systems Through Decision Tree and Random Forest Models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0200111" target="_blank" >RIV/00216305:26220/26:0200111 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/EMCSIPI52291.2025.11169813" target="_blank" >http://dx.doi.org/10.1109/EMCSIPI52291.2025.11169813</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/EMCSIPI52291.2025.11169813" target="_blank" >10.1109/EMCSIPI52291.2025.11169813</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Parameterized Surrogate Models of Electromagnetic Systems Through Decision Tree and Random Forest Models

Popis výsledku v původním jazyce

Simulations are an integral part of the electromagnetic systems' design and optimization process. Mathematical models associated with them are usually very large, time and memory-consuming. It is useful to have faster surrogate models with an accuracy comparable to full-order models, especially in an optimization process. For this purpose, we propose to use Decision Trees and Random Forests as parametrized surrogate models of electromagnetic systems. We focus on approximating the parameter-dependent transfer functions using Decision Trees and Random Forests. We used limited data of the module of transfer functions obtained from PEEC simulations to train models appropriately sampled in the parameter space with Latin Hypercube sampling. Trained models predict Transfer Function Modules at any parameter sample in the design space domain with good accuracy.

Název v anglickém jazyce

Parameterized Surrogate Models of Electromagnetic Systems Through Decision Tree and Random Forest Models

Popis výsledku anglicky

Simulations are an integral part of the electromagnetic systems' design and optimization process. Mathematical models associated with them are usually very large, time and memory-consuming. It is useful to have faster surrogate models with an accuracy comparable to full-order models, especially in an optimization process. For this purpose, we propose to use Decision Trees and Random Forests as parametrized surrogate models of electromagnetic systems. We focus on approximating the parameter-dependent transfer functions using Decision Trees and Random Forests. We used limited data of the module of transfer functions obtained from PEEC simulations to train models appropriately sampled in the parameter space with Latin Hypercube sampling. Trained models predict Transfer Function Modules at any parameter sample in the design space domain with good accuracy.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10300 - Physical sciences

Projekt

—

Návaznosti

R - Projekt Ramcoveho programu EK

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

IEEE International Symposium on Electromagnetic Compatibility

ISBN

9798331508746

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

35-39

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

—

Místo konání akce

Raleigh, North Carolina, USA

Datum konání akce

18. 8. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—