During SPIGL’20…

Poster communication on Thermodynamics-based Artificial Neural Networks at Joint Structures and Common Foundations of Statistical Physics, Information Geometry and Inference for Learning at Ecole de Physique des Houches:

Full-length pre-print here

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About Filippo

Filippo Masi is currently post-doc at GeM Laboratory (École Centrale de Nantes). He develops novel theoretical and numerical tools focusing on material modeling with Thermodynamics-based Artificial Neural Networks. His main research topics are: data-driven and machine learning approaches for the constitutive modeling of materials, the structural and fast-dynamic behavior of masonry structures, and geomechanics. He received the PhD thesis award by the French Computational Structural Mechanics Association (CSMA) and of the prize for the best PhD thesis bringing technological and conceptual breakthroughs in the industry by Centrale Innovation, in 2021.

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