Bridging scales: At the crossroads among renormalisation group, multi-scale modelling, and deep learning

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Machine learning will define the 21st Century: from simple image classification to text generation and decision making, its impact on society will be nothing but immense. At present, the detailed mechanisms behind the power of AI still evade our understanding; growing evidence, however, suggests that it is possible to rationalise how deep learning works in terms that are very familiar to theoretical physicists, that is, the renormalisation group. The systematic, hierarchical coarsening of detailed information into increasingly simpler and more collective features is a cornerstone of modern physics, and it can be leveraged not only to make sense of machine learning’s baffling capabilities, but also and most importantly to steer its development. This workshop will explore the area where theoretical physics of soft and condensed matter and deep learning overlap, looking for novel and more powerful tools to model, investigate, and understand the world around us.


Emmi supported workshop (


  • Roberto Menichetti (University of Trento and INFN-TIFPA)
  • Francesco Pederiva (University of Trento and INFN-TIFPA)
  • Raffaello Potestio (University of Trento and INFN-TIFPA)
  • Alessandro Roggero (University of Trento and INFN-TIFPA)


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