Altair Feko is a leading solution for high-frequency electromagnetic simulation, trusted globally for over two decades in industries such as aerospace, defense, automotive, communications, and consumer electronics. It is designed to solve complex connectivity, compatibility, and radar challenges that arise in an increasingly connected world. Feko provides a comprehensive suite of parallelized solvers that are hybridized for optimal performance in designing connected products, ensuring electromagnetic compatibility (EMC), and addressing radar technologies. Feko excels in various applications, including antenna simulation, wireless coverage, network planning, spectrum management, and radar cross-section (RCS) analysis. The tool helps design robust products by optimizing wireless connectivity (e.g., 5G networks), performing electromagnetic interference (EMI) and compatibility (EMC) assessments, and supporting radar simulation. It integrates seamlessly with other Altair tools like WRAP™ for spectrum management, WinProp™ for wireless coverage, and newFASANT for radome modeling, leveraging machine learning to optimize system performance and reduce simulation time. Features:

• One Product, Multiple Solvers: Feko combines various solvers to handle different types of high-frequency electromagnetic simulations, ensuring broad applicability and cross-validation.
• True Solver Hybridization: Uses multiple hybrid solvers to efficiently solve complex, electrically large problems that cannot be addressed by a single solver.
• Solver Accuracy and Performance: Features extensive validation and continual development of solver performance, offering GPU-based acceleration and parallel scaling for optimized computational efficiency.
• Specialized Solutions: Includes CMA solvers for complex modeling needs, along with specialized solutions for cable modeling, interference, ultrasonic systems, and virtual testing.
• Complete Connectivity Workflow: Offers a unique workflow that integrates antenna performance, radio coverage, and planning analysis to optimize wireless connectivity.
• Design Optimization with Machine Learning: Leverages machine learning and design of experiments (DOE) to accelerate antenna design and placement optimization.