About us

Thanks to our competencies in topological optimization, we make learning algorithms much better and easier to use than the state of art methods.

We offer breakthrough deep learning solutions for:

  • Long-term prediction (for almost chaotic systems)
  • System with distributed parameters (get rid of curse of dimensionality).

These products are integrated in our Deep Quantitative Learner framework.

Fields of applications

System control

– New command laws, based on our long-term prediction tools           – Effective robust control

Reliability and security

– Mechanical fatigue assessment                         – Uncertainty quantification

Decision support

– Development of patient specific models for medical          applications                        – Risk assessment

Production and maintenance optimization

– Preventive maintenance                   – Planning models

Highlights

Topologically optimized neural network (focus on your core business)
100%
Learning physics and complex system behavior from data
100%
Mastering of dynamic and nonlinear behavior
100%

Deep quantitative learner framework

deepROM®

deepROM® is our neural network factory. It excels at figuring out automatically the optimal network topology. The whole process is then standalone and avoids overlearning. Hence the user only has to provide the learning data and shortly after gets a reliable model. See examples here.

xROM™

xROM™ is our implementation of recurrent reduced order models. It builds models for nonlinear dynamic processes and get rid of their inherent instabilities. 

 

 

zROM®

zROM® (for adaptively generated simulation data) is a nonintrusive tool that builds a reduced order model of the frequency response of a system from learning data. zROM® controls adaptively the data generation process.  See an example in the field of mechanics.

coROM™

Our convolutional neural network solution is adapted to higher dimensional problems. It gets rid of the so-called curse of dimensionality. It controls adaptively the data generation process. See examples here.

AdagosOpt™

AdagosOpt™ is our optimization tool based on reduced order modeling. It is suitable for strongly nonlinear behavior and for quantitative or qualitative discrete variables. It controls adaptively the data generation process. See an application to telecomSee an application to the design of a power plant.

AdagosFit™

AdagosFit™ is a hierarchical learning process whose aim is to model high dimensional functions. It controls adaptively the generation, by a solver, of learning data.

Our partners

What our clients say

En rupture totale par rapport à l’état de l’art, la société ADAGOS arrive à créer des modèles réduits ayant un grand nombre de paramètres (plusieurs centaines dans les cas considérés). Cela veut dire que ces outils ne semblent pas être affectés par ce que l’on appelle la malédiction de la dimension (curse of dimensionality). Cela parait d’autant plus étonnant que nos paramètres varient dans des intervalles très larges et ont un caractère discret aussi bien qualitatif que quantitatif.

Jean-Marie Hamy Technical Project Manager and Design Authority @ AREVA NP

Les enjeux sont considérables pour les systèmes embarqués et les applications médicales. De ce fait, nous maintenons une veille technologique et nous sommes en contact avec les universités les plus prestigieuses pour scruter la moindre évolution dans le domaine. Mais, il n’était pas nécessaire d’aller chercher bien loin, l’innovation de rupture peut venir d’une petite startup toulousaine appelée ADAGOS.

Michel Rochette Director of research @ Ansys France

Our team

9 avenue de l'Europe - 31520 Ramonville-Saint-Agne