Programa del congreso

Computational electromagnetics (CEM) has become essential in aerospace and naval engineering, particularly for analyzing electromagnetic interference (EMI) and compatibility (EMC) in defense applications involving complex platforms. As hyper-sensorization trends emerge, the need for enhanced CEM methods becomes evident to manage electromagnetically dense environments while meeting strict EMC/EMI and radiation hazard (RADHAZ) requirements. Confidentiality concerns pose challenges in information sharing among international partners, complicating the design of subsystems. This work proposes an EPA-based domain decomposition scheme that encapsulates electromagnetic interactions of large-scale platforms using small-scale Huygens’ surfaces. The method significantly improves efficiency and accuracy in the design of antennas and sensors onboard platforms without requiring detailed geometric knowledge. Additionally, it allows assessment of antennas’ contributions to radar cross-section (RCS) while maintaining safety and confidentiality standards. Integrated with advanced techniques based on the domain decomposition method and multitrace (MT) formulations, this approach also ensures robust modeling of near-field interactions among encapsulated objects.

In this work we use the Reduced Basis Method (RBM) as a parametric reduced order model for electromagnetic problems depending on multiple variables. The RBM approach carries out a simultaneous fast sweep in two variables, namely, the frequency and the dielectric permittivity in a dual cavity sensor. This device is aimed to detect dielectric permittivities in the X-band and accurate fast electromagnetic simulations are critical to enhance the sensor performance. This method enables fast EM simulations while maintains the same accuracy as other high-fidelity full-wave methods such as the Finite Element Method.

This work introduces a preliminary exploration of the Finite Element-Boundary Integral (FEBI) method for addressing the scattering problem in open regions. The FEBI method discretizes the wave equation in a region surrounding the scattering object and sets a truncation boundary by imposing that the fields radiated by the currents on the object surface fulfill a Robin-type boundary condition. This, unlike more common approaches such as Absorbing Boundary Conditions (ABCs) or Perfectly Matched Layers (PMLs), allows an asymptotically exact truncation condition with no spurious effects such as internal reflections. Also, it allows placing the truncation boundary very close to the object surface—thus reducing the computational domain—while supporting concave or disjoint boundaries. The formulation presented here is quite general and aims to offer insight into the formal aspects of the method. We also present proof-of-concept examples to illustrate some features of the method, such as the preconditioning.

Efficient and accurate electromagnetic simulations are crucial for the design and optimization of modern microwave devices. However, as the complexity of these models increases, traditional finite element method simulations become computationally expensive, particularly for parametric analyses involving multiple frequency sweeps and material variations. To address this challenge, this work proposes a parametric reduced basis method (pRBM) to accelerate frequency-domain simulations while maintaining the accuracy of the solution. The proposed approach reduces the computational cost by constructing a reduced order model that captures the electromagnetic dynamics of the high-dimensional system.

The effectiveness of the pRBM is demonstrated through the parametric analysis of an inline dielectric resonator microwave filter. Considering angular frequency and dielectric permittivity as parameters, the method efficiently computes frequency responses with a significant reduction in computational effort. These results highlight the potential of model order reduction techniques for accelerating parametric electromagnetic simulations, making them valuable for iterative design processes and large-scale engineering applications.

Time domain electromagnetic (EM) simulations play a crucial role in understanding electromagnetic field behavior across various engineering and scientific applications. However, traditional numerical techniques, including the finite element method in the time domain (FEMTD), require the solution of large systems of equations at each time step, leading to high computational costs. To address this challenge, a reduced order modeling approach based on dynamic mode decomposition (DMD) is proposed. Dominant spatiotemporal patterns are efficiently extracted from FEMTD solutions using DMD, allowing for the construction of a reduced order model (ROM) that significantly accelerates simulations while preserving accuracy. Using an initial set of FEMTD solutions, the DMD-based ROM enables the reconstruction of the EM response at arbitrary time instances with reduced computational effort. This approach also provides valuable information on the dominant structures that govern the evolution of the EM field. The numerical results demonstrate the effectiveness of DMD in reducing computational complexity while maintaining high-fidelity solutions.

This work explores the integration of Artificial Intelligence (AI) models to predict key components of the simulation pipeline of the Fast Multipole Method (FMM).