Rethinking antenna placement: a complex puzzle
In space engineering, every design decision can have major consequences. One of the most complex challenges is antenna placement. This process requires balancing multiple constraints: ensuring optimal signal performance, reducing weight, and integrating components seamlessly into a satellite or aircraft structure. Yet, traditional methods rely on lengthy simulations, slowing down the innovation process.
To address this challenge, we collaborated with the Centre National d’Études Spatiales (CNES) to explore an innovative approach: AI simulation.
Accelerating design cycles with AI simulation
The promise of AI simulation is to overcome the inefficiencies caused by slow simulation cycles. In concurrent engineering, where multiple teams work simultaneously, delays in simulation can disrupt workflows and hinder decision-making. AI-driven simulation addresses this challenge by transforming historical simulation data into actionable models that provide results in seconds. This approach enhances efficiency, accelerates iterations, and fosters seamless collaboration.
Unlike traditional methods that depend on repetitive and time-consuming testing, AI simulation significantly accelerates the process. By exploring the design space extensively, it delivers more informed results in less time, allowing engineers to concentrate on high-value tasks while keeping pace with the demands of concurrent engineering.
Addressing the data challenge: a long-term investment
One common concern with AI simulation is the need to generate large volumes of synthetic data for model training, as historical data is often unavailable. This additional step, along with the time required to train models, can initially seem to lengthen the overall design cycle—making the promise of “faster time-to-design” appear less compelling in the short term.
However, this shift represents an investment rather than a burden. By systematically building structured and reusable datasets, companies create a foundation that enhances simulation accuracy and accelerates future iterations. Unlike traditional simulation-based workflows, where data is generated on demand and then discarded, fostering a data-driven culture enables organizations to continuously refine their AI models. This approach not only unlocks efficiency gains over time but also positions AI-powered design as a sustainable competitive advantage.
🔗Explore how businesses can overcome the data challenge and maximize the benefits of AI simulation. Read full article.
A successful Proof of Concept (PoC)
In 2024, our collaboration with CNES led to a promising evolution: a successful PoC demonstrating the transformative potential of AI simulation in electromagnetic simulation for antenna placement. By automating and optimizing simulations, we have taken a significant step toward reducing iteration cycles while improving the performance of the proposed solutions.
Breaking simulation silos: How Miura enables seamless AI integration in space engineering
Engineering teams in the space industry often struggle with fragmented simulation ecosystems, where proprietary software limits interoperability and flexibility. Most AI solutions reinforce these constraints, making their integration a challenge. Miura stands apart by offering a fully interoperable and relying on open formats and standards, designed to fit seamlessly into existing workflows. This ability to integrate effortlessly with diverse tools and platforms was a key factor in our successful collaboration with CNES, where our technology aligned ideally with their demanding processes. By ensuring organizations retain full control over their data and models while leveraging the power of AI, Miura provides a scalable and future-proof approach to engineering simulation, eliminating vendor lock-in and unlocking new possibilities for innovation.
What’s next: tackling new space challenges
Building on this initial success, we are now preparing to take on even more ambitious challenges in 2025. The goal is to expand the application of AI simulation to other complex space problems. By refining our methods and broadening our scope, we are paving the way for a new standard of efficiency and innovation.
An evolution in space engineering
AI simulation is not just an incremental improvement, it represents a paradigm shift in how space systems are designed. While its initial adoption requires investment in data management, our infrastructure ensures that companies build a growing knowledge base, continuously accelerating design cycles.
This collaboration with CNES marks a key milestone in this transformation. It demonstrates that, with an innovative approach and AI-driven technology, we can unlock a future where engineers spend less time on lengthy and repetitive processes—and more time on creativity and innovation.
