Abstract
This paper proposes a novel technique for automated neural network based multiphysics parametric modeling of microwave components. For the first time, we propose to utilize automated model generation (AMG) algorithm in the field of electromagnetic (EM) centric multiphysics parametric model development to improve the neural-based multiphysics modeling efficiency. All the subtasks in developing a neural network based multiphysics parametric model, including EM centric multiphysics data generation, neural network structure adaptation, training and testing, are integrated into one unified and automated framework, thus converting the conventional human-based manual modeling into an automated computational process. In the proposed algorithm, automated EM centric multiphysics data generation is realized by automatic driving of multiphysics simulation tools. Parallel computation technique is incorporated to further speedup the data generation process by driving multiple EM centric multiphysics simulations on parallel computers simultaneously. In addition, automated neural model structure adaptation algorithm for multiphysics parametric modeling is also proposed. In this way, the proposed technique automates the neural-based multiphysics model development process and significantly reduces the intensive human effort and modeling time demanded by the conventional manual multiphysics modeling methods. The achieved neural model can be used to provide accurate and fast prediction of the EM centric multiphysics responses of microwave components in high-level multiphysics design. Examples of multiphysics parametric modeling of two microwave filters are presented to show the advantage of this work.
Highlights
With the increasing accuracy requirements, electromagnetic (EM) centric multiphysics parametric modeling is becoming more and more important and necessary for high performance microwave component and system design
Besides the EM domain, other physics domains such as thermal and structural mechanics are needed to be taken into consideration in EM centric multiphysics parametric modeling, to provide accurate EM behavior evaluation of microwave components and systems in a real-world multiphysics
We propose to use automated model generation (AMG) techniques [17] in multiphysics parametric modeling area to automate the neural-based multiphysics parametric model development process and improve the multiphysics modeling efficiency
Summary
With the increasing accuracy requirements, electromagnetic (EM) centric multiphysics parametric modeling is becoming more and more important and necessary for high performance microwave component and system design. W. Na et al.: Automated Neural Network-Based Multiphysics Parametric Modeling of Microwave Components models which represent the thermal effects. ANNs can represent general nonlinear relationship between EM behavior of microwave components and the geometrical parameters after a proper training process. The neural-based multiphysics parametric modeling in [18] is a step-by-step manual process, which involves sequential multiphysics data generation, neural network selection, training and testing. This multiphysics parametric modeling process is manually carried out and requires intensive human effort and experience. All the subtasks in developing a neural-based multiphysics parametric model, including EM centric multiphysics data generation, neural network structure adaptation, training and testing, are integrated into one unified and automated framework. Where w is a vector containing the neural network weights
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.