Multiple scattering of waves is a complex phenomenon that exists in physical system. The Multiple scattering theory (MST) was initially studied by Foldy, Lax, Korringa and Ishimaru. Past theoretical work was based on analytic methods such as vector radiative transfer theory, Feynman diagrams, higher order Kirchhoff theory , AIEM etc. Our group recently advanced MST through efficient three-dimensional full-wave numerical solutions of 3D Maxwell’s equations. Surface scattering simulations are based on the multilevel SMCG method . Volume scattering simulations are based on fast solutions of Foldy Lax multiple scattering equations. Accuracies of results have been validated by comparisons with commercial softwares. This paper reviews the recent results in: 1) Surface scattering by soil fractal surfaces and high wind ocean surfaces , with rms heights up to 3 wavelengths , showing backscattering enhancement and large cross polarization , 2) Volume scattering by forests and crop fields with several hundred trees and more than a thousand crops in the domain 3) wave scattering by metasurfaces with thousands of scatterers above a substrate and 4) band diagrams of photonic crystals with complex scatterers .

Classical electromagnetics has been with us for over 150 years. We can view 1865 as the harbinger of classical electromagnetics during when Maxwell added the displacement current to complete the four Maxwell’s equations. It was in 1927 that Dirac introduced the quantum version of electromagnetics which he also called quantum electrodynamics (QED). We have called this quantum electromagnetics in line with the quantum optics community where we are more interested in developing quantum technologies, and the wavelengths can be many atomic spacing large.
Classical electromagnetics followed by computational electromagnetics have been with us for decades. With it, we have sleuth of modern technologies that have transformed our modern world. The most obvious is the emergence of wireless communications and the development of microchips and nanotechnologies.
While quantum electromagnetics has not been with us for as long, its potential for impact is tremendous. Thus, it presages ways to convert a classical solution into one that is quantum compliant. With this pathway, we can exploit the wealth of knowledge in classical electromagnetics, especially computational electromagnetics, to model quantum systems.
In this talk, we will discuss such possibilities, and how classical solutions can be used to model quantum weirdness.

This keynote talk presents an overview of higher order computational electromagnetics (CEM), which historically was mainly an intellectual curiosity, and discusses advancements that have made it a practical tool for antennas and propagation (AP) developments and applications. Higher order CEM uses higher order current/field basis functions defined on large (e.g., on the order of a wavelength in each dimension) curvilinear geometrical elements, which greatly reduces the number of unknowns for a given problem and enhances the accuracy and efficiency of the analysis. Although it has been around for almost 60 years, there has been a gap between the academic and scientific interest in higher order CEM techniques, which evidently show great numerical potential, and their actual use in electromagnetics research and engineering applications. One way to contribute to closing this gap is to make higher order CEM methodologies, elements, and approximation functions more comprehensible and approachable, and more easily and confidently used by both CEM developers and practitioners, with as much automation and certainty as possible. To this end, this talk discusses a synergistic combination of error estimation and control, meshing, adaptive refinement, hp-methods, and uncertainty quantification for higher order CEM. We go from theoretical backgrounds and numerical implementations to practical applications of higher order CEM in AP designs and systems.

The growing demands of modern wireless communication technologies impose new challenges on antenna design and simulation. Antenna systems of increasing complexity, capable of delivering multiple dynamically accessible functionalities, are now essential to enhance capacity and support diverse roles in contemporary communications and sensing platforms.
This presentation focuses on the concept of multi port, multi mode antennas, in which several independent radiating modes operating within the same frequency band are generated in a shared physical volume. These uncorrelated modes, tightly packed within a compact form factor, can significantly improve the agility and capacity of communications links through pattern diversity or Multiple Input Multiple Output (MIMO) operation.
Within this framework, the talk will outline techniques for efficiently realizing multiple independent modes in a limited volume. It will then illustrate how computational tools and global optimization algorithms can be leveraged to enable advanced beamforming by exploiting a large number of co located radiating modes. This approach supports full hemisphere beam steering and nulling with high consistency and across any polarization. The presented concept offers promising new opportunities for future distributed sensing and communication systems.

Simulation-driven design changed product development forever, enabling engineers to reduce design, iterations, and prototype testing. Increasing scientific computing power expanded the opportunity to apply analysis, making large design studies possible within the timing constraints of a program. This talk will focus on advanced CEM simulation tools that incorporate numerical methods, such as Method of Moments (MoM), Multilevel Fast Multipole Method (MLFMM), Finite Element Method (FEM), Finite Difference Time Domain (FDTD), Physical Optics (PO), Ray Lunching Geometrical Optics (RL-GO), and Uniform Theory of Diffraction (UTD). As the complexity of connected devices increases each day, designers are taking advantage of AI/ML to generate trained models for their physical antenna designs and perform fast and intelligent optimization on these trained models. Using the trained models, different optimization algorithms and goals can be run quickly, in seconds, that can be utilized for comparison studies, stochastic analysis for tolerance studies etc. Use of cloud computing combined with AI/ML, many design iterations can be performed in a short period and reducing the time to market. This talk will also focus on future trends in cloud computing for physics-based simulations and the emerging topics such as Digital Twins.

The evolution of wireless communication systems towards 6G and beyond is driving a fundamental paradigm shift: the transition from traditional, passive wireless channels to Smart Electromagnetic Environments (SEME). Unlike conventional networks that treat the physical propagation channel as a static and uncontrollable entity, a SEME actively molds and controls electromagnetic (EM) wave propagation to drastically enhance network performance. At the core of this technological revolution is Computational Electromagnetics (CEM). However, transforming the SEME concept into a tangible reality introduces unprecedented computational and design complexities that cannot be tackled by traditional engineering approaches alone.
More specifically, the successful realization of a Smart EM Environment fundamentally relies on the synergistic integration of advanced EM predictors and robust optimizers. A dual-scale computational framework is strictly required, operating simultaneously at the microscopic level (device design) and the macroscopic level (network planning and deployment).
At the device level, the combination of accurate CEM predictors (ranging from rigorous full-wave solvers to fast AI-driven surrogate models) and powerful optimization algorithms is essential for the synthesis of key enabling technologies. Modern networks rely on highly complex, multi-scale devices, including Smart Programmable Electromagnetic Surfaces (SP-EMS), Reconfigurable Intelligent Surfaces (RIS), Smart Repeaters, Integrated Access and Backhaul (IAB) nodes, and next-generation Base Transceiver Stations (BTS). Optimizers are deployed to systematically explore vast architectural design spaces, tuning unit cells, meta-atoms, and array configurations. Concurrently, predictors evaluate the scattering and radiation properties in real-time, ensuring that the designed devices can dynamically manipulate EM waves with high fidelity, high energy efficiency, and low latency.
Beyond the synthesis of individual hardware components, the predictor-optimizer synergy is critical during the macroscopic network planning and deployment phase. Fast and accurate EM predictors—such as advanced ray-tracing models integrated with machine learning—are necessary to evaluate the propagation characteristics of complex, electrically large urban or indoor environments. When coupled with system-level optimizers, these predictive tools enable the strategic placement, orientation, and real-time configuration of RIS, IAB nodes, and BTS across the territory.
The ultimate goal of combining CEM predictors and optimizers is to solve highly complex, multi-objective engineering problems. By accurately modeling the physical environment and intelligently deploying customized network nodes, it becomes possible to strictly minimize energy consumption and deployment costs (CAPEX and OPEX). Simultaneously, this methodology allows operators to maximize overall EM coverage, signal-to-interference-plus-noise ratio (SINR), and the Quality of Service (QoS) perceived by the end-users. Ultimately, an advanced computational framework driven by the continuous loop of accurate prediction and smart optimization is the absolute cornerstone for designing sustainable, cost-effective, and high-performance Smart EM Environments.

Computational Electromagnetics (CEM) is a scientific discipline positioned at the intersection of electrical engineering, high-performance computing, and applied mathematics. It is primarily concerned with the modeling and simulation of complex electromagnetic phenomena that arise in advanced technological and scientific contexts. Historically, CEM has provided the predictive framework underlying a wide spectrum of applications in electrical and electronic engineering, optics, wireless communications, geophysical exploration, and biomedical systems.
In recent years, the scope of CEM has broadened significantly, engaging with an expanding range of interdisciplinary fields, including information retrieval, computational neuroscience, machine learning, and brain-computer interfaces. A pervasive trend across these domains is the substantial growth in the dimensionality and scale of the problems to be addressed, driven by system miniaturization and increased component density. This evolution has led to escalating levels of computational complexity and, consequently, increased cost and effort associated with electromagnetic modeling, design, and simulation.
This talk will provide a comprehensive overview of the major challenges both longstanding and emerging, facing the field of CEM, with examples spanning from canonical problems to those of "Holy Grail"-like complexity. Emphasis will be placed on recent research efforts aimed at achieving paradigm shifts in simulation methodologies, with the objective of overcoming the computational bottlenecks inherent in conventional approaches and enabling significant performance gains. Applications discussed will include brain imaging and modeling, electromagnetic dosimetry, epilepsy diagnosis, and the development of brain-computer interfaces.

Multiscale and multiphysics numerical solvers for the analysis of complex three‑dimensional (3D) structures must be able to handle hybrid meshes consisting of tetrahedra, hexahedra, prisms, and quadrilateral‑based pyramids, even in the presence of curved geometries. Such meshes are widely adopted because they exploit the specific advantages of each element type.
In structured or mildly curved regions, hexahedra and prisms typically offer higher accuracy and better computational efficiency, whereas tetrahedra naturally accommodate highly complex geometries. Prisms are also particularly effective in boundary‑layer discretizations, where anisotropic refinement is required.
Pyramids, on the other hand, play a crucial role as transition elements: they are the only elements capable of consistently bridging mesh regions with triangular faces to those with quadrilateral faces. This transition cannot be achieved using prisms alone, which map triangles to triangles and quadrilaterals to quadrilaterals, keeping quadrilateral faces aligned with the extrusion direction and triangular faces transverse to it.
Over the past thirty years, together with several collaborators, we have developed interpolatory vector bases and high‑order hierarchical vector bases for all major two‑ and three‑dimensional element types, including—more recently—complete high-order bases for pyramidal elements. The inclusion of pyramidal bases has enabled unrestricted and conforming h‑refinement across hybrid meshes.
For surface elements, we have also introduced singular additive bases specifically designed to capture field singularities at edges and corners with high fidelity.
This presentation will review the main results obtained so far and outline several promising directions for future developments in applied electromagnetics.

The role of Electromagnetic (EM) fields in our lives has been increasing. Communication, remote sensing, integrated command/ control/surveillance systems, intelligent transportation systems, medicine, environment, education, marketing, and defense are only a few areas where EM fields have critical importance. We have witnessed the transformation from Engineering Electromagnetics to Electromagnetic Engineering for the last few decades after being surrounded by EM waves everywhere. Among many others, EM engineering deals with broad range of problems from antenna design to EM scattering, indoor–outdoor radiowave propagation to wireless communication, radar systems to integrated surveillance, subsurface imaging to novel materials, EM compatibility to nano-systems, electroacoustic devices to electro-optical systems, etc. The range of the devices we use in our daily life has extended from DC up to Terahertz frequencies. We have had both large-scale (kilometers-wide) and small-scale (nanometers) EM systems. A large portion of these systems are broadband and digital and must operate in close proximity that results in severe EM interference problems. Engineers must take EM issues into account from the earliest possible design stages. This necessitates establishing an intelligent balance between strong mathematical background (theory), engineering experience (practice), and modeling and numerical computations (simulation).

This Distinguished/keynote lecture aims at a broad-brush look at current complex EM problems as well as certain teaching / training challenges that confront wave-oriented EM engineering in the 21st century, in a complex computer and technology-driven world with rapidly shifting societal and technical priorities.