Artificial Intelligence plays an increasingly critical role in the development of complex systems. Its effectiveness hinges on learning, which requires large volumes of annotated data or consistent interaction with an environment. In many industrial contexts, meeting these conditions is costly, difficult, or even impossible. Simulation helps overcome these limitations by generating representative data and virtual environments tailored for the training of algorithms.
Simulation is becoming a cornerstone in the design of highly efficient AI, particularly in supervised learning and reinforcement learning.
Leveraging simulation for radar recognition
Supervised learning relies on labelled datasets to train a model to recognize or predict elements based on examples. In radar image recognition, the variety of conditions is extensive: different angles, object orientations, weather impacts, and electromagnetic noise must all be accounted for.
Gathering such comprehensive real-world data is not only costly in resource but also limited by technical feasibility and operational constraints. Simulation addresses this challenge by producing radar images based on accurate physical modeling of wave propagation and materials. It offers full control over every scenario parameter, ensuring consistency and precision in annotations.
This enables the rapid generation of large, tailored datasets, the ability to simulate rare or edge cases, and early validation of model robustness before any real-world testing. Simulation thus enhances learning performance, traceability, and repeatability.
Training AI for navigation via reinforcement learning
In contrast to supervised learning, reinforcement learning uses trial and error: an agent interacts with an environment, experiments with different actions, and learns to maximize a reward based on outcomes.
This method is particularly suited to autonomous navigation, where systems must learn to locate themselves, avoid obstacles, and adapt to unexpected changes. Simulation provides a flexible, immersive environment where the agent can explore safely.
Virtual environments enable extensive scenario coverage, accelerate learning processes, and support training in hazardous conditions without operational risk. Simulation also facilitates detailed observation of the agent’s behavior, strategy refinement, and decision-making improvements.
Expertise in building robust intelligent systems
At Scalian, simulation is integrated into a structured development process involving modelling, validation, and algorithm optimization. The objective is not only to accelerate learning but also to ensure seamless transferability between simulation and real-world deployment, while designing reliable, resilient, and explainable systems.
Whether optimizing radar signatures or enabling autonomous navigation, simulation has become a strategic foundation in the advancement of AI for critical applications. It enables a shift beyond traditional experimentation, unlocking higher performance, greater safety, and stronger control over complex intelligent systems.
We will share our expertise in simulation at the 55th International Paris Air Show – Le Bourget 2025. Come and meet us to find out more!
