Medical imaging and advanced therapy products demand absolute precision. At Siemens Healthineers, improving product accuracy and autonomy while reducing operator workload are constant priorities. But how do you efficiently model highly complex, unpredictable physics—like shifting mechanical friction or moving cables—when traditional first-principle equations fall short?
In this video, Clément Coic from the Innovation Department at Siemens Healthineers shares how they are leveraging Dyad to solve these exact engineering challenges. By combining first-principle physics models with Scientific Machine Learning (SciML) and Universal Differential Equations (UDEs), they are able to "rediscover" the complex physics of their medical devices without starting from scratch.
Discover how Dyad’s integration into the Julia ecosystem unlocks powerful geometric optimization, multi-body dynamic optimization for collision avoidance, and how Dyad’s new AI agent is radically speeding up engineering workflows.
