ADAGOS has developed NeurEco; a new neural network approach based on parsimony.
NeurEco reduces the  resources (size of learning data, energy consumption, size of neural network and memory requirement, computing time, development time) required to implement machine learning methods, by several orders of magnitude when compared with the current state of the art. The topological gradient theory, introduced by the founders of ADAGOS, allows us to create these parsimonious neural networks automatically. 

Impacts of parsimony:
•	Deep learning of continuous phenomena - Unlike the state of the art, which is oriented to discrete responses, parsimonious neural networks are suitable for both continuous and discrete responses and are capable of capturing the physical or biological phenomena conveyed by the data.
•	Ability to create reliable complex dynamic models - This can be achieved even in the case of quasi-chaotic phenomena, where parsimony is of critical importance and any redundancy would have irremediable consequences on the quality of the model.
•	Robustness – our neural networks are capable of resisting "deepfool" attacks, which can provide a significant challenge for the redundant state-of-the-art models.
•	Embedding of artificial intelligence algorithms to meet the challenges of real-time control of complex systems ("Digital Twin");
•	Processing of hybrid data (simulation / measurements).
Based on its innovative technology, ADAGOS designs and realizes, in collaboration with its customers, business tools or specific models for a wide range of applications (industry, transport, energy, health, ...).

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