Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
Please note that all times are shown in the time zone of the conference. The current conference time is: 13th June 2024, 11:22:34pm CEST
Multi-objective Optimization of Inductors Based on Neural Network
Xiaohan Kong, Hajime Igarashi
Graduate School of Information Science and Technology, Hokkaido University, Japan
This work proposes a method to perform multi-objective optimization of inductors using a surrogate model based on neural network (NN) instead of finite element method (FEM). Traditional design methods require repetitive FEM calculations, resulting in a very long overall design time. In contrast, this work utilizes a well-trained neural network to predict magnetic core loss, the volume and saturation current, avoiding repetitive FEM evaluations and significantly reducing the total time for inductor optimization.
Learning to solve Electromagnetic Problems: a Comparison among Different Machine Learning Approaches
Alessandro Formisano1, Mauro Tucci2
1Università della Campania "Luigi Vanvitelli", Italy; 2Università di Pisa, Italy
The possibility of adopting data-driven procedure to create a model of electromagnetic problems has been investigated since long. Recently, the availability of high-performance computing systems even at desktop level has provided different model of “artificial intelligence” processors able to deal with such a problem, examples being Physically-Informed Neural Networks, Generative Adversarial Networks. Etc. In this study, using a simple yet representative benchmark problem, some of the most common solutions are compared, with the aim of highlighting respective advantages and drawbacks.
1:50pm - 2:10pm ID: 102 / Oral Session 2: 3 Abstract submission for on-site presentation Topics: Application, Algorithms Keywords: Eddy currents, Gain tuning, Impulse response, Levitation devices, Model order reduction
Optimal Parameter Estimation for Conductor Movement Using Cauer Ladder Network Representation and Virtual Time-response Based Iterative Gain Evaluation and Redesign
1Kindai University, Japan; 2Doshisya University, Japan; 3Kyoto University, Japan
This article propose an optimal design of control gain parameters using a Cauer ladder network with constant basis functions and an Impulse-response model based V-Tiger. We model the TEAM Workshop Problem 28 by the Cauer ladder network method, which is a model order reduction technique, and enable the analysis by a circuit simulator such as Simulink. We determine the control gain parameters by applying the Impulse-response model based V-Tiger to the data obtained from the analysis. This method is useful for efficiently determining control parameters on simulation.
Globalized High-Frequency Optimization Using Inverse Models
Slawomir Koziel1,2, Anna Pietrenko-Dabrowska2
1Reykjavik University; 2Gdansk University of Technology
This paper discusses a novel technique for quasi-global parameter tuning of high-frequency structures using response features and inverse surrogate models. Our approach enables a low-cost identification of the most promising parameter space regions, followed by a fine tuning by means of local routines. The presented method is validated using several high-frequency structures. Its global search capability and computational efficiency are demonstrated by extensive comparisons with multiple-start local search as well as nature-inspired optimizers.