RFID Merlion Forum 2024 Paper Sharing Series - Knowledge-Empowered Intelligent Electromagnetic Design
The deepening research of physics-informed neural networks (PINNs) demonstrates the advantages of this method to electromagnetic inverse design. Some studies have deeply embedded the physics information into the neural networks, obtaining better solution performance and providing a new way for the PINN electromagnetic inverse design problems. This article explores PINN with embedded analytical models (EAM-PINN) to design waveguide devices. We first develop a multilayer dielectric-loaded rectangular waveguide model and derive its S21 parameter expressions. Then, we embed the S21 parameter expressions, as an S21 analytical model, into the traditional PINN framework, reducing the scale of loss functions. The EAM-PINN implements three inverse design cases: a known analytical solution retrieving, a bandpass filter, and a dispersive delay line. Finally, we conclude the advantages of EAM-PINN compared to various current methods and validate our inverse design results through simulation.
Prof. Ren Wang earned his B.S. and Ph.D. degrees in electronic information science and technology and radio physics from the University of Electronic Science and Technology of China (UESTC), Chengdu, in 2014 and 2018, respectively. He worked as a Visiting Scholar at the Optoelectronics Research Centre, University of Southampton, U.K., from September 2016 to September 2017. Currently, he holds the position of Associate Professor at UESTC. His research interests encompass antennas, electromagnetic wave propagation, and time-reversed electromagnetics.
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- College of Design and Engineering
- National University of Singapore
- Singapore, Singapore
- Singapore 117583
- Building: Block E4
- Room Number: E4-04-05, E-Cube 1
- Starts 19 February 2024 07:00 AM UTC
- Ends 26 February 2024 09:00 AM UTC
- No Admission Charge
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Knowledge-Empowered Intelligent Electromagnetic Design
The deepening research of physics-informed neural networks (PINNs) demonstrates the advantages of this method to electromagnetic inverse design. Some studies have deeply embedded the physics information into the neural networks, obtaining better solution performance and providing a new way for the PINN electromagnetic inverse design problems. This article explores PINN with embedded analytical models (EAM-PINN) to design waveguide devices. We first develop a multilayer dielectric-loaded rectangular waveguide model and derive its S21 parameter expressions. Then, we embed the S21 parameter expressions, as an S21 analytical model, into the traditional PINN framework, reducing the scale of loss functions. The EAM-PINN implements three inverse design cases: a known analytical solution retrieving, a bandpass filter, and a dispersive delay line. Finally, we conclude the advantages of EAM-PINN compared to various current methods and validate our inverse design results through simulation.
Prof. Ren Wang earned his B.S. and Ph.D. degrees in electronic information science and technology and radio physics from the University of Electronic Science and Technology of China (UESTC), Chengdu, in 2014 and 2018, respectively. He worked as a Visiting Scholar at the Optoelectronics Research Centre, University of Southampton, U.K., from September 2016 to September 2017. Currently, he holds the position of Associate Professor at UESTC. His research interests encompass antennas, electromagnetic wave propagation, and time-reversed electromagnetics.
Biography:
Prof. Ren Wang earned his B.S. and Ph.D. degrees in electronic information science and technology and radio physics from the University of Electronic Science and Technology of China (UESTC), Chengdu, in 2014 and 2018, respectively. He worked as a Visiting Scholar at the Optoelectronics Research Centre, University of Southampton, U.K., from September 2016 to September 2017. Currently, he holds the position of Associate Professor at UESTC. His research interests encompass antennas, electromagnetic wave propagation, and time-reversed electromagnetics.
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