Active digital twins via active inference
M Torzoni, D Maisto, A Manzoni, F Donnarumma,
G Pezzulo, and A Corigliano
This research introduces “active digital twins”, built upon the neuroscience-inspired active inference paradigm. The proposed closed-loop perception–action framework integrates reasoning, decision-making, and learning, enabling digital twins to autonomously balance goal-directed and information-seeking behaviors. Demonstrations in structural health monitoring and predictive maintenance scenarios highlight how uncertainty-resolving actions help maintain synchronization between physical assets and their digital counterparts.
Mastering Truss Structure Optimization With Tree Search
G Garayalde, L Rosafalco, M Torzoni, A Corigliano
Truss structures must satisfy mechanical requirements and practical constraints related to fabrication, transportation, and assembly. These factors make truss design a highly constrained problem, where traditional optimization methods often struggle due to high computational demands and slow convergence. This paper introduces an innovative approach that frames truss design as a sequential decision-making problem, addressed through reinforcement learning combined with generative grammar rules. By emulating a continuous human-computer interaction and effectively balancing the exploration and exploitation of design alternatives, this method outperforms traditional techniques in computational efficiency for structural design tasks.
