Nas, AbdurrahimKankilic, SuedaKarpat, Esin2025-10-102025-10-102025979833151482210.1109/ISAS66241.2025.111019502-s2.0-105014943279https://doi.org/10.1109/ISAS66241.2025.11101950https://hdl.handle.net/20.500.14627/1193Electromagnetic absorbers prevent the reflection and transmission of electromagnetic waves. Electromagnetic absorbers have a wide range of applications from military to medical applications. In these areas, absorber designs have different importance in terms of parameters such as reflection coefficient, selected material and thickness. Many difficulties are encountered to achieve the optimal design. In this paper, we propose a machine learning regression method for three-layer microwave absorber architecture to obtain the optimum parameters, overcome the difficulties and speed up the process. The material and thickness of each layer are used as parameters to feed the models and the reflection coefficient is estimated using these parameters. Predictions are made with various regression algorithms. These algorithms are KNeighbors Regression, Random Forest Regressor, XGBoost Regression, CatBoost Regressor, AdaBoost Regressor which uses similarities between observations, Gradient Boosting Regressor which is tree based or boosted tree based algorithms, Linear Regression which uses a linear model, Partial Least Squares Regression which uses cross decomposition, Gaussian Process Regressor which uses statistical distribution, Stochastic Gradient Descent Regressor which uses a linear model to reduce empirical loss to predict an output. Mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), R-squared (R2) are used with the predictions of each model to obtain the metrics for the analysis of the results. The predicted values and actual values of the metrics are used to compare the regression algorithms used in the research. After the comparison, our observations show that in most cases CatBoost Regressor is better than other models used in the research. In general, it is observed that most of the machine learning regression algorithms used in this paper can be used to predict the reflection coefficient of three-layer microwave absorbers as output and input parameters used in the research. © 2025 Elsevier B.V., All rights reserved.eninfo:eu-repo/semantics/closedAccessElectromagnetic InterferenceMachine LearningMultilayer Microwave AbsorbersRegressionAdaptive BoostingDecision TreesElectromagnetic Wave AbsorptionElectromagnetic Wave InterferenceElectromagnetic Wave ReflectionForestryGaussian DistributionGradient MethodsLinear RegressionMachine LearningMean Square ErrorMedical ApplicationsMultilayersRandom ForestsSignal InterferenceStochastic ModelsWave TransmissionElectromagnetic AbsorbersElectromagneticsLinear ModelingMachine LearningMicrowave AbsorbersMultilayer Microwave AbsorberReflection and TransmissionRegressionRegression AlgorithmsThree-LayerElectromagnetic PulseForecastingLearning SystemsStochastic SystemsConference Object