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ENGINEERING

Effectively implementing intelligence algorithms on embedded systems is a real challenge.

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Our goal is to speed up design processes and to reduce time to market:

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  • NeurEco creates Reduced Order Models (ROMs) from a small amount of simulation data

  • It goes beyond ROMs by creating a real-time copy of a complex high-fidelity simulation tool, from a few simulation results​

  • Beyond learning the data, NeurEco learns the underlying rules; it happens that the NeurEco model is more in line the laws of physics than the data itself

  • This is the basis of a robust long-term dynamic prediction

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OUR SUCCESS STORIES

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​Model reduction
techniques

NeurEco enables real-time AI modeling for nuclear reactors, managing complex dynamics while minimizing data and build time without sacrificing accuracy.

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Nuclear core cooling system

 

NeurEco reduces complex core cooling simulations from 100 to 3 coefficients, enabling real-time decision support.

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​Optimal parameters of an antenna

NeurEco optimizes antenna design with real-time modeling, speeding up time to market and enhancing performance.

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Neutronic fluxes prediction

NeurEco predicts neutronic fluxes with accuracy, improving risk management in complex, nonlinear contexts.

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