Physics-based grey-box models bridging Machine Learning and Scientific Computing


  • Data-driven model order reduction
  • Data-driven discovery of differential equations
  • Neural Networks-based acceleration of numerical methods
  • Physics-Informed Neural Networks
  • Surrogate models and emulators

The research carried out at MOX creates a concrete bridge and synergies between Machine Learning and Scientific Computing: we complement physics-based methods and  data-driven models, to improve knowledge of real phenomena. For example, models based on PDEs can serve as regularizers for Machine Learning algorithms, and, conversely, data-driven methods can complement traditional models where knowledge of physics is lacking or not fully understood.