%0 Conference Paper %1 StilligJavier.2022.FeasibilityStudyonMachine %A Stillig Javier, %A Parspour Nejila, %A Ewert Daniel, %A Jung Thomas Josef, %B 2022 International Seminar on Intelligent Technology and Its Applications (ISITIA) %D 2022 %K CET;Inductance Calculation;Machine Learning;Wireless Power Transfer hp_iew %P 193--198 %R 10.1109/ISITIA56226.2022.9855321 %T Feasibility Study on Machine Learning-based Method for Determining Self-and Mutual Inductance %X This paper presents a method for inductance calculation of coils based on a machine learning algorithm. To show the feasibility of the approach, we generate a set of artificial training data describing a configuration of two planar spiral coils in varying dimensions and positions to each other in order to calculate their self- and mutual inductance. Afterwards, the data is used to train and evaluate three different machine learning models. Our evaluation shows that multiple linear regression with polynomial features reaches almost the same precision as the reference FASTHENRY2, but is orders of magnitude faster. With this novel machine learning based algorithm we enable new applications, where real-time prediction of inductances or coupling factors is advantageous.

@inproceedings{StilligJavier.2022.FeasibilityStudyonMachine, abstract = {This paper presents a method for inductance calculation of coils based on a machine learning algorithm. To show the feasibility of the approach, we generate a set of artificial training data describing a configuration of two planar spiral coils in varying dimensions and positions to each other in order to calculate their self- and mutual inductance. Afterwards, the data is used to train and evaluate three different machine learning models. Our evaluation shows that multiple linear regression with polynomial features reaches almost the same precision as the reference FASTHENRY2, but is orders of magnitude faster. With this novel machine learning based algorithm we enable new applications, where real-time prediction of inductances or coupling factors is advantageous.}, added-at = {2022-10-27T13:21:39.000+0200}, author = {{Stillig Javier} and {Parspour Nejila} and {Ewert Daniel} and {Jung Thomas Josef}}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2c213ef81647d87f83e09a6f03277dc1e/iew_homepage}, booktitle = {2022 International Seminar on Intelligent Technology and Its Applications (ISITIA)}, doi = {10.1109/ISITIA56226.2022.9855321}, file = {Stillig Javier, Parspour Nejila et al. 2022 - Feasibility Study on Machine Learning-based:S\:\\Mitarbeiter\\01_Bibliothek\\01_Literatur\\Citavi Attachments\\Stillig Javier, Parspour Nejila et al. 2022 - Feasibility Study on Machine Learning-based.pdf:pdf}, interhash = {9a4859fb6a6de42b58d89f0e206f5513}, intrahash = {c213ef81647d87f83e09a6f03277dc1e}, keywords = {CET;Inductance Calculation;Machine Learning;Wireless Power Transfer hp_iew}, pages = {193--198}, timestamp = {2022-10-27T11:30:50.000+0200}, title = {Feasibility Study on Machine Learning-based Method for Determining Self-and Mutual Inductance}, year = 2022 }