Artificial Neural Networks for High-Frequency Design

Slawomir Koziel received the M.Sc. and Ph.D. degrees in electronic engineering from Gdansk University of Technology, Poland, in 1995 and 2000, respectively. He also received the M.Sc. degrees in theoretical physics and in mathematics, in 2000 and 2002, respectively, as well as the PhD in mathematics in 2003, from the University of Gdansk, Poland. He is currently a Professor with the Department of Engineering, Reykjavik University, Iceland. His research interests include CAD and modeling of antennas and microwave circuits, simulation-driven design, surrogate-based optimization, machine learning, and artificial intelligence. He has published over 30 book chapters and over 1,300 peer-reviewed research papers. He is a founder and director of Engineering Optimization & Modeling Center at Reykjavik University. Slawomir Koziel is a recipient of Fulbright Scholarship for the academic year 2003/2004. He has served on the Editorial Board of various international journals, program committee member as well as co-organizer of numerous special sessions and workshops at international conferences. He is a Chief Editor of Int. J. Antennas Propag., an Associate Editor of several journals (IET Microwaves Ant. Prop., El. Lett., Int. J. Math. Modeling Num. Opt., Int. J. Numerical Modeling). Dr. Koziel is an IEEE Fellow. He also placed 54 (out of over 200,000) on the 2024 list of top 2% scientists by Stanford University in the field of Networking & Telecommunications. His h-index is 71 with >24,600 citations.Anna Pietrenko-Dabrowska received the M.Sc. and Ph.D. degrees in electronic engineering from Gdansk University of Technology, Poland, in 1998 and 2007, respectively. Currently, she is a Full Professor with Gdansk University of Technology, Poland. She has served as the Associate Editor of Int. J. Num. Modeling, Int. J. Ant. Propag., and Electronics. She has been a guest co-editor of several special issues ISI-ranked journals including Int. J. Num. Modeling, Electronics, and Applied Sciences. She has been a program committee member of international conferences (IEEE MTT-s Int. Conf. Num. EM and Multiphysics Modeling and Optim., Int. Conf. Comp. Science, IEEE Int. Symp. Ant. Propag.). She is a co-author of the monographs Response feature technology for high-frequency electronics. Optimization, modeling, and design automation (Springer, 2024), and Performance-driven surrogate modeling of high-frequency structures (Springer, 2020), and the Editor of Surrogate modeling for high-frequency design: recent advances (World Scientific, 2022). Her research interests include simulation-driven design, design optimization, experiment design, control theory, modeling of microwave and antenna structures, numerical analysis. Since 2021, she consistently made the list of top 2% scientists by Stanford University in the field of Networking & Telecommunications. She published over 250 peer-reviewed research papers. Her h-index is 31 with over 3600 citations.

• Artificial Intelligence in Microwave and Antenna Systems: Enabling Smarter Workflows.- Artificial Intelligence Enabled Real Time Failure Recovery of Microstrip Patch Antenna Arrays.- Parametric Modeling of Electromagnetic Behaviors Using Neuro-TF Techniques for High-Frequency Design.- EM Design Optimization using Neuro-TF Surrogates for High-Frequency Design.- Multi-Fidelity Forward Electromagnetic Modeling and Neural Networks.- Multi-Fidelity Neural and Surrogate Models for Electromagnetic Inverse Problems and Digital Twins.- Bayesian Neural Network-Assisted Antenna Design Exploration Technique and Applications.- A Physics-Informed Deep Operator Network for 3-D Time-Domain Electromagnetic Modeling.- Automated Neural-Network-Based Model Generation Algorithms for Microwave Applications.- Consensus Deep Neural Networks, Ensembles of DNNs and Mixture of Experts for Antenna Design.- Modeling of Antenna Electrical and Field Characteristics Using Transformer-Inspired Convolutional Neural Networks.- Digital Predistortion Topologies with Neural Network-Assisted Coefficient Estimators for Highly Efficient Power Amplifiers.- Accurate Data-Driven Antenna Modeling Using Recurrent Neural Networks and Dimensionality Reduction.- Autonomous Deep Learning Frameworks for High-Fidelity Behavioral Modeling of Microwave Transistors..- Artificial Neural Network-Assisted Multi-Surrogate Optimization Framework for High-Frequency Antenna Design.- Machine Learning Strategy for Modeling Dielectric Dispersive Permittivity.- AI-Driven Design for Millimeter-Wave Antenna Arrays.